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Personal and Indoor PM2.5 Exposure from Burning Solid Fuels in Vented and Unvented Stoves in a Rural Region of China with a High Incidence of Lung Cancer
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Personal and Indoor PM2.5 Exposure from Burning Solid Fuels in Vented and Unvented Stoves in a Rural Region of China with a High Incidence of Lung Cancer
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† Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
‡ Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht 3508 TD, The Netherlands
§ China National Environmental Monitoring Center, Beijing 100029, People’s Republic of China
∥ College of Public Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand
⊥ Chinese University of Hong Kong, Hong Kong, People’s Republic of China
*Phone: +31 30 253 9448; fax: +31 30 253 9449; e-mail: [email protected]
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Environmental Science & Technology

Cite this: Environ. Sci. Technol. 2014, 48, 15, 8456–8464
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https://doi.org/10.1021/es502201s
Published July 8, 2014

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Abstract

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The combustion of biomass and coal is the dominant source of household air pollution (HAP) in China, and contributes significantly to the total burden of disease in the Chinese population. To characterize HAP exposure related to solid fuel use and ventilation patterns, an exposure assessment study of 163 nonsmoking female heads of households enrolled from 30 villages was conducted in Xuanwei and Fuyuan, two neighboring rural counties with high incidence of lung cancer due to the burning of smoky coal (a bituminous coal, which in health evaluations is usually compared to smokeless coal—an anthracite coal available in some parts of the area). Personal and indoor 24-h PM2.5 samples were collected over two consecutive days in each household, with approximately one-third of measurements retaken in a second season. The overall geometric means (GM) of personal PM2.5 concentrations in Xuanwei and Fuyuan were 166 [Geometric Standard Deviation (GSD):2.0] and 146 (GSD:1.9) μg/m3, respectively, which were similar to the indoor PM2.5 air concentrations [GM(GSD):162 (2.1) and 136 (2.0) μg/m3, respectively]. Personal PM2.5 was moderately highly correlated with indoor PM2.5 (Spearman r = 0.70, p < 0.0001). Burning wood or plant materials (tobacco stems, corncobs etc.) resulted in the highest personal PM2.5 concentrations (GM:289 and 225 μg/m3, respectively), followed by smoky coal, and smokeless coal (GM:148 and 115 μg/m3, respectively). PM2.5 levels of vented stoves were 34–80% lower than unvented stoves and firepits across fuel types. Mixed effect models indicated that fuel type, ventilation, number of windows, season, and burning time per stove were the main factors related to personal PM2.5 exposure. Lower PM2.5 among vented stoves compared with unvented stoves and firepits is of interest as it parallels the observation of reduced risks of malignant and nonmalignant lung diseases in the region.

Copyright © 2014 American Chemical Society

Subjects

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Introduction

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More than 60% of the population in China is rural. Nearly all of this population use biomass and coal fuels for their day to day cooking and heating, the combustion of which is the dominant source of household air pollution (HAP) in China and contributes significantly to the total burden of ill health in the Chinese population. (1) Particulate matter with an aerodynamic diameter smaller than 2.5 μm (PM2.5) is one of the main pollutants in solid fuel smoke and is closely associated with many of the adverse health effects associated with HAP. (2-9)
Xuanwei and Fuyuan, two neighboring counties in Yunnan province, China have a mostly rural population, and have increased rates of nonmalignant and malignant lung disease associated with HAP from solid fuel combustion. (6, 10) In particular, the lung cancer rates in this region are among China’s highest in both males and females regardless of smoking status. (11) Previous epidemiological studies have shown that the excess lung cancer risk could be mainly attributed to the domestic combustion of “smoky” coal in poorly ventilated homes for heating and cooking. (11-13) Coal is the major domestic fuel in this region and depending upon the source (residents typically purchased coal from their nearest mines) is either referred to as smoky or smokeless coal, terms which relate to the amount of visible smoke produced on combustion. Smoky coal is further divided into subtypes based upon the underlying geochemical properties and geographic location. (14, 15) These subtypes may have varying carcinogenic potentials as a wide variation in lung cancer risk (up to 25-fold) has been observed between geographic locations in Xuanwei. (16) Additionally, stove improvement programs have resulted in a reduction in HAP and a decreased burden of nonmalignant and malignant lung disease in this region, (12, 17, 18) thus suggesting that HAP is important in the etiology of malignant and nonmalignant respiratory disease in the study area.
There have been few systematic indoor air quality studies in Xuanwei and Fuyuan to date, and these have mainly focused on the quantification of airborne benzo[a]pyrene (BaP). (13, 19) Moreover, these studies focused on indoor air measurements, and it is unclear how these relate to personal exposure. This work presents our findings from personal and indoor PM2.5 measurements collected from 30 villages throughout the Xuanwei and Fuyuan counties. Particular attention will be paid to differences between solid fuels (especially differences between smoky and smokeless coal), differences between stove designs (e.g., vented and unvented), and variation between smoky coal subtypes. This study also provides an opportunity to characterize the relationship between indoor and personal measurements, which may have an influence on costs, participation, and other logistical concerns for long-term studies (18, 20) and provides information on the efficiency of stove improvement programs.

Materials and Methods

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Study Design

This exposure assessment study was designed to provide a comprehensive characterization of air contaminants and exposures related to the use of solid fuels for cooking and heating, coordinating with an ongoing large case-control study of lung cancer, and a cross-sectional molecular epidemiology study of lung cancer in the same area.
A total of 30 villages were selected to represent the major geographical regions in Xuanwei and Fuyuan (Figure 1). In each selected village, up to 5 households were selected according to the following criteria, which was intended to allow for eventual comparability to the larger case-control study: (i) having a stove that used solid fuel; (ii) the residence was more than 10 years old; (iii) use of the same cooking/heating equipment for the past 5 years; and (iv) presence of a nonsmoking healthy female aged 20–80 who was primarily responsible for cooking. The exposure survey was carried out in two phases. Phase I was carried out from August 2008 to February 2009 with all 30 villages visited and 148 subjects enrolled. Phase II was carried out between March and June of 2009. Sixteen of the initial 30 villages, chosen to represent the geographical spread, fuel use, and stove type distribution to phase I were revisited (Figure 1). In the second phase, 15 new subjects were enrolled and 53 of the enrollees from phase I were revisited allowing estimation of seasonal effects. Each sampling phase consisted of two sequential 24-h air measurements during which indoor and personal measurements were taken.

Figure 1

Figure 1. Villages selected for the exposure survey and coal mines in Xuanwei and Fuyuan counties. Classification of coal regions based on the State Standard of China Coal Classification (GB5751–86); 1/3 coking, coking, gas fat, and meager lean coals are subtypes of smoky coal.

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PM2.5 Measurement

PM2.5 measurements for 24-h were collected on preweighed 37 mm Teflon filters using a cyclone with an aerodynamic cutoff of 2.5 μm (model BGI, GK 2.05SH) at a flow rate of 3.5 L/min (±20%). Pumps were calibrated prior to all measurements and flow rates were recorded pre and post sampling. Data were not accepted for further analysis if the post sampling flow was not within 10% of the presampling flow. For personal measurements, the pump was packed in a hip bag and the cyclone was attached near the breathing zone. At night, the sampling bag was put next to the subject’s bed.
Most houses had a single living/cooking area. Measurements of 24-h indoor PM2.5 were collected in the main living area using the same methods as described for the personal measurements. The samplers were placed at least 0.25m from the walls and between 1 and 2m from the stove. Placement varied somewhat because of limited available space in some households. If a subject had a separate room with an additional stove for cooking or heating, then a second stationary measurement was taken (this represents 6% of indoor measurements).
Exposed filters were packed individually in Petri slides sealed in zipped amber plastic bags and stored at −80 °C before postweighing. Particulate mass was assessed by pre- and postweighing of the filters in an environmentally controlled weighing room using a microbalance at 1 μg accuracy. Each filter was pre- and postweighed in duplicate. If the duplicate measurements differed by more than 5 μg, then the filter was reweighed. Weights were divided by the volume of air drawn through the filters to calculate PM2.5 concentrations (μg/m3).
For quality control purposes, approximately 10% of households were randomly selected to have duplicate PM2.5 measurements collected. The coefficient of variation of this quality control was 13%, based on 26 pairs of collocated indoor PM2.5 samples.

Household Interview and Measurement

Household interviews were conducted by two trained interviewers. House dimensions were recorded and a sketch of each household was made detailing the position and dimensions of stoves, windows, chimneys, doors, and stairways. Subjects’ activities during the sampling periods were recorded by administering a short activity questionnaire. The activity survey included information on cooking activities, heating practices, fuel usage, outdoor activities, and sleeping habits. Stove details were recorded with a particular focus upon the ventilation aspect of their design. Traditionally, fuel has been burned in unvented firepits, but in recent decades a variety of stove types have been utilized throughout the area, not all of which include a functioning chimney. The major stove designs encountered were as follows: vented stoves, unvented stoves, firepits, portable stoves (a stove design intended to be lit outdoors and then carried indoors for use) and a combination of these stove designs. The other major sources of ventilation recorded were the numbers of doors, windows, and the presence or absence of a stairway in the main cooking area.
Fuel types were reported by respondents. Coal was generally reported as either “smoky” or “smokeless”. Coal types were confirmed by petrochemical analysis of collected coal samples and in 11 cases the classification of coal type was changed to match petrochemical analysis (7 samples were reclassified as smoky coal and 4 samples were reclassified as smokeless coal). (14) Other categories identified were as follows: wood, plant products (which included the burning of corn cobs and tobacco stems–sometimes also in combination with wood), “mixed” coal (which represented the burning of manufactured coal briquettes and combinations of briquettes, smoky and smokeless coal), and “mixed” fuel (which represented the burning of combinations of wood, plant materials and coal). The coal mines supplying coal were ascertained at interviews during household visits. Smoky coal burned during the exposure measurements came from 5 coal mines in Xuanwei and 8 mines in Fuyuan. Smoky coal was divided into subtypes according to the parameters of the State Standard of China Coal Classification (GB5751-86). (15) These smoky coal subtypes, based upon the parameters of volatile matter on a dry ash free basis and caking index, are referred to as coking, 1/3 coking, gas fat, and meager lean coals.
On each measuring day, weather parameters including outside temperature, humidity, rainfall, wind speed, and direction were monitored by a portable weather station set up in the center of each village.

Statistical Analysis

Normal probability plots indicated that exposures could best be described by a log-normal distribution. Therefore, natural logarithms of exposure concentrations were used in the statistical analyses. Exposure levels were summarized as arithmetic means (AM), geometric means (GM), and geometric standard deviations (GSD). Analysis of variance (ANOVA) testing was performed to test for differences in PM2.5 exposure between differing stove and fuel configurations and for variation within the designated smoky coal subtypes. Tukey Honestly Significant Difference (HSD) testing was performed to assess pairwise differences within each combination of two levels of the various fuel and stove configurations.
A linear mixed effect model was constructed to identify variables that contributed to personal PM2.5 exposure. Subjects and villages were assigned as random effects with a scalar (variance component) covariance structure. Multiple variables were considered for inclusion as fixed effects including: stove design, fuel type (including both broad fuel categories and the inclusion of smoky coal subtypes), fuel source, weight of fuel used, meteorological conditions, room size, number of owned stoves, number of doors, windows and the presence or absence of a stairway in the main cooking room, hours of using stoves, and the season during which measurements were taken. A full list of the 32 considered variables is available in Supporting Information (SI) Table S1. The variables selected for inclusion in the final model were those which best contributed to the prediction of PM2.5 measurements and contributed to the lowest Akaike information criterion (AIC) score. The model can be summarized using the following formula:where yijf represents the natural log transformed value of personal PM2.5 exposure being modeled for village i, person j on day f; μ represents the intercept value (i.e., the log transformed PM2.5 value for the reference group); β1 through to βn represent the fixed effect variable coefficients for variables x1 through xn; bIi represents the random effect coefficient for village i, while bJij represents the random effect coefficient for subject j from village i; and εijf represents the error for subject j in village i on day f.
Spearman correlation was calculated between personal and indoor PM2.5 measurements collected on the same day and a linear mixed effect model was constructed to identify which variables best explained any differences in the association between personal and indoor PM2.5 measurements.

Results

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A total of 163 subjects participated in the exposure survey and 216 household visits were conducted. An overview of the characteristics of the study population is available in Table 1. Smoky coal was the main fuel type used during the measurement in both counties (45.8%), followed by “mixed” fuels (25.9%), and smokeless coal (8.8%). The most commonly used stove design was vented stoves (34.7%) followed by the use of multiple stoves with differing ventilation designs [which we refer to as “mixed ventilation” (28.2%)]. The average age of the subjects in the exposure survey was 56 years. The distribution of the villages visited is shown in Figure 1.
Table 1. Characteristics of the Study Population in Xuanwei and Fuyuan
 phase Iphase IIall
 XuanweiFuyuanXuanweiFuyuan 
subjects, n74743137163a
villages, n15158830
age (in 2009), mean ± SD54.0 ± 14.956.7 ± 13.762.0 ± 11.358.9 ± 12.256.0 ± 14.4
stove type, n(%)     
vented stove34(45.9)19(25.7)12(38.7)10(27.0)75(34.7)
high stove with chimney13(17.6)5(6.8)6(19.4)1(2.7)25(11.6)
low stove with chimney8(10.8)11(14.9)2(6.5)6(16.2)27(12.5)
multiple stoves with chimneys13(17.6)3(4.1)4(12.9)3(8.1)23(10.6)
unvented stove4(5.4)12(16.2)0(0.0)11(29.7)27(12.5)
high stove without chimney0(0.0)6(8.1)0(0.0)2(5.4)8(3.7)
low stove without chimney0(0.0)0(0.0)0(0.0)1(2.7)1(0.5)
multiple stoves without any chimney4(5.4)6(8.1)0(0.0)8(21.6)18(8.3)
portable stove2(2.7)19(25.7)1(3.2)8(21.6)30(13.9)
firepit3(4.1)7(9.5)3(9.7)3(8.1)16(7.4)
mixed ventilation stovesb30(40.5)13(17.6)13(41.9)5(13.5)61(28.2)
unknown ventilation stove1(1.4)4(5.4)2(6.5)0(0.0)7(3.2)
solid fuel type, n(%)     
smoky coal42(56.8)32(43.2)19(61.3)6(16.2)99(45.8)
smokeless coal0(0.0)13(17.6)1(3.2)5(13.5)19(8.8)
“mixed” coalc9(12.2)5(6.8)1(3.2)4(10.8)19(8.8)
wood3(4.1)1(1.4)2(6.5)6(16.2)11(5.1)
plant materialsd4(5.4)3(4.1)1(3.2)0(0.0)9(4.2)
“mixed” fuele16(21.6)18(24.3)6(19.4)16(43.2)56(25.9)
unknown0(0.0)2(2.7)1(3.2)0(0.0)3(1.4)
median length of stove operation, hours per day43.38135.1
a

There were 216 visits to the households in total: of the 148 subjects visited in phase I, 53 were revisited the second time, and 15 new subjects were enrolled in phase II.

b

Refers to the use of vented stove and unvented stove/portable stove simultaneously.

c

Refers to the use of combinations of smoky and smokeless coal and also to the use of prepared coal briquettes.

d

Plant materials include combinations of wood, tobacco stem and corncob.

e

Refers to combinations of wood, plant materials and coal.

The overall GM of personal PM2.5 concentrations in Xuanwei and Fuyuan were 166 (GSD:2.0) and 146 (GSD:1.9) μg/m3, respectively. These levels were similar to the overall indoor PM2.5 concentration [GM(GSD):162 (2.1) and 136 (2.0) μg/m3 for Xuanwei and Fuyuan, respectively]. Personal and indoor PM2.5 measurements correlated well, with Spearman correlation analysis indicating a moderately high coefficient between personal and indoor PM2.5 (r = 0.70, P < 0.0001). Personal measurements were generally higher than indoor measurements (median percentage difference 6.5%). Linear mixed effect models were constructed, using the same approach as detailed above in order to assess which variables contributed to the observed differences between the log of the personal and indoor measurements. They observed that colder temperatures and the use of more fuel (as measured by weight) contributed to higher personal measurements, relative to indoor measurements (SI Table S2).
Descriptive statistics showed variations of PM2.5 measurements between the various fuel types and stove designs (Table 2). In general, burning wood [GM(GSD):289 (2.1) and 327 (1.9) μg/m3] or plant materials [GM(GSD):225 (2.1) and 276 (2.6) μg/m3] resulted in the highest personal and indoor PM2.5 levels, respectively. Significantly higher indoor PM2.5 concentrations were observed among smoky coal burning homes compared to homes using smokeless coal [GM(GSD):144 (2.0) versus 96 (1.6) μg/m3, p < 0.05]. Personal PM2.5 exposure attributable to people using smoky coal was also higher than those using smokeless coal [GM(GSD):148(1.9) versus 115(1.9) μg/m3], although the exposure did not differ significantly.
Table 2. Personal and Indoor PM2.5 (μg/m3) Exposure Related to Different Stove Ventilation Configurations and Fuel Types
  personalindoor
fuel typestove designNaAMbGMbGSDbNaAMbGMbGSDb
smoky coal 206180148c1.9210185144c2.0
 vented stove1101501341.61141491271.7
 unvented stoved82522331.682211832.0
 portable stove221781431.9201681352.0
 firepit15307277e1.615371350e1.4
 mixed ventilation stove442191642.3452321662.1
smokeless coal 471521151.94510496f1.6
 vented stove51511262.051171041.7
 unvented stove181671092.1171071031.4
 portable stove191501231.918101891.8
 firepit31041021.3391901.3
 mixed ventilation stove297951.3285831.4
“mixed” coalg 381831611.7421641302.0
 vented stove131521371.7141511231.9
 unvented stove0   0   
 portable stove142091801.8141731212.4
 firepit21561501.521571541.3
 mixed ventilation stove91921761.6121701451.8
wood 24369289f2.124393327f1.9
 vented stove82261831.983392572.2
 unvented stove0   0   
 portable stove63273201.352472441.2
 firepit105083922.4105204671.7
 mixed ventilation stove0   0   
plant materialsh 13284225c2.113417276f2.6
 vented stove31231091.8380761.4
 unvented stove34164081.334023771.6
 portable stove24394391.024224071.5
 firepit41461381.546173823.0
 mixed ventilation stove1605605NA1658658NA
“mixed” fuelI 94205160c2.0113210152c2.2
 vented stove191211041.822140982.3
 unvented stove17306250e2.226316220e2.5
 portable stove72192031.572041961.3
 firepit0   0   
 mixed ventilation stove472071651.9541961531.9
a

Data for unknown ventilation stoves or unknown fuel type are not shown.

b

AM = Arithmetic Mean, GM = Geometric Mean, GSD = Geometric Standard Deviation.

c

Significant (p < 0.05) variation between stove ventilation designs within designated fuel type via ANOVA testing.

d

Refers to high and/or low stoves without any chimney.

e

p < 0.05 when compared with vented stove in same fuel type via Tukey HSD test.

f

p < 0.05 when compared with smoky coal via Tukey HSD test.

g

Refers to the use of combinations of smoky, smokeless coal, and prepared coal briquettes.

h

Plant materials include combinations of wood, tobacco stem, and corncob.

I

Refers to combinations of wood, plant materials and coal.

When assessing the role of stove design, we observed that measurements from homes and individuals using vented stoves were generally lower than if an unvented stove or firepit was used. Notably, among smoky coal burning homes, vented stoves had significantly lower personal and indoor PM2.5 exposures as compared with firepits [personal values: GM(GSD):134 (1.6) versus 277 (1.6) μg/m3, p < 0.05; indoor values: 127(1.7) versus 350(1.4) μg/m3, p < 0.05]. Vented stoves were also observed to have significantly lower PM2.5 levels than unvented stoves for homes burning “mixed” fuels [personal measurements: 104 (1.8) versus 250 (2.2) μg/m3, p < 0.05; indoor measurements: 98(2.3) versus 220(2.5) μg/m3, p < 0.05].
An assessment of variation in PM2.5 levels between and within designated smoky coal subtypes is presented in Table 3. Only households that exclusively burned smoky coal were included in the analysis (199 personal and 210 indoor measurements). ANOVA analysis revealed little variation between the coking coal mines from Xuanwei but showed significant variation between the four designated smoky coal subtypes for both personal and indoor measurements (coking coal, 1/3 coking coal, gas fat coal, and meager lean coal) in Fuyuan. Furthermore, significant variation in personal PM2.5 levels was observed for coal sourced from within the coking coal, 1/3 coking coal and gas fat coal mines in Fuyuan (indoor measurements showed significant variation in the coking and gas-fat coal subtypes).
Table 3. Personal and Indoor PM2.5 (μg/m3) Concentrations from Smoky Coal Burning Homes from Xuanwei and Fuyuan, by Coal Source
   personalindoor
countysmoky coal subtypecoal mineNaAMbGMbGSDbNAMGMGSD
Xuanweicoking coal 1191891532.01221961492.0
  Azhi342271811.9331861601.7
  Baoshan122101682.2122461932.1
  Laibin281531322.1302201522.2
  Tangtang311941522.0331911332.3
  Yangchang141421251.6141351241.5
Fuyuanoverall 80168142c1.888169138c1.9
 coking coal 23213175d1.927188154d1.9
  Daping91111041.51087831.4
  Enhong92412081.8112502221.7
  Haidan53483291.462422211.7
 1/3 coking coal 13183165d1.6122712451.7
  Bagong102071941.492742621.4
  Dahe3104961.632632002.8
 gas fat coal 40135120d1.639117107d1.6
  Housuo381301161.6371111021.5
  Qingyun22372371.022362351.1
 meager lean coalGumu4138962.841961492.0
a

Number of measurements is from households which exclusively burn smoky coal and report a coal source consistent with reported coal type.

b

AM = Arithmetic Mean, GM = Geometric Mean, GSD = Geometric Standard Deviation.

c

Significant (p < 0.05) variation between smoky coal subtypes sourced in Fuyuan via ANOVA test.

d

Significant (p < 0.05) variation between coal mines within identified smoky coal subtype via ANOVA test.

Linear mixed effect modeling was carried out to identify variables that had a significant role in personal PM2.5 exposures. Of the 32 variables considered, 5 were found to significantly impact personal PM2.5. These determinants are the broad fuel types (smoky coal, smokeless coal, etc.), stove ventilation, the season of the year, the number of windows in the main cooking area, and the recorded number of hours burning a solid fuel standardized by the number of stoves used. Estimates (β), 95% confidence intervals, and geometric mean ratio [GMR = exp(β) = GM(estimate)/GM(reference)] are provided in Table 4.
Table 4. Linear Mixed Effect Modeling of ln-Transformed Personal PM2.5 Exposure
 estimate (β)95% CIGMRa
fuel type   
smokeless coalref. 1.00
smoky coal0.270.02,0.521.31
“mixed” coal0.350.06,0.641.42
wood1.030.66,1.402.80
plant materials0.430.02,0.841.54
“mixed” fuel0.370.11,0.631.45
stove design   
vented stoveref. 1.00
unvented stove0.480.22,0.741.62
portable stove0.260.06,0.471.30
firepit0.380.10,0.661.47
mixed ventilation stove0.20.03,0.361.22
unknown ventilation stove–0.34–0.77,0.090.71
number of windows in main cooking room   
noneref. 1.00
one0.220.01,0.441.25
two–0.01–0.26,0.230.99
season   
autumnref. 1.00
winter0.190.02,0.361.21
spring–0.24–0.41,-0.070.79
summer–0.34–0.68,0.000.71
number of hours burning fuel standardized by number of used stovesb0.010.003,0.031.01
variation explained, %   
between individual subjects 35 
between villages 79 
reference valuec, ln-μg/m3 4.35 
a

Geometric mean ratio = GM(estimate)/GM(reference) = Exp(β).

b

Median period 4.3 h; IQR 2.2 to 9.6 h per stove.

c

Reference value represents base value of log transformed PM2.5 in model for reference group (smokeless coal burnt in a vented stove, during autumn in a room with no windows).

The linear mixed effect model of personal PM2.5 explains 35% of the variance between subjects and 79% of the variance between villages (Table 4). Among the assessed fuel types, wood results in the highest modeled PM2.5 exposure (GMR: 2.80). Among stove designs, unvented stoves were found to result in the highest predicted PM2.5 exposure (GMR: 1.62) although the use of firepits resulted in similar predicted PM2.5 exposure (GMR: 1.47). Homes with one window in their main cooking area were found to result in higher PM2.5 exposure (GMR for one window: 1.25) than those with zero or two windows (GMR for two windows: 0.99). The season during which measurements were taken also played a role in PM2.5 exposure, with measurements taken in summer resulting in the lowest predicted PM2.5 exposure (GMR: 0.71) and measurements taken in winter resulted in the highest (GMR: 1.21). The amount of time burning fuels, standardized by the number of stoves used, also played a role in PM2.5 exposure with every hour of stove operation [median operating time 4.3 h; Interquartile Range (IQR) 2.2 to 9.6 h per stove] resulting in an incremental increase (GMR: 1.01) of PM2.5 exposure. The model output for indoor measurements is very similar to personal measurement model, shown in SI Table S3.

Discussion

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The combustion of biomass and coals is a significant contributor to HAP and exposure to PM2.5 worldwide. Biomass burning may account for 74–87% total PM2.5 in households with a single dominant cooking source. (21) A wide range of factors, including stove design, fuel type, activity patterns, weather conditions, and household room configuration, can contribute to HAP. (20) For example, fuel type and ventilation were found to be significant determinants of household PM2.5 in a study based in India, (22) while a Chinese based stove emission study showed that honeycomb coal resulted in lower emissions of sulfur dioxide, nitrogen oxide, and total suspended particulate, but 2–3 fold higher PM2.5, compared to coal cake. (23) In our study on personal and indoor PM2.5 exposure in a rural population in China with high lung cancer incidence, fuel type, ventilation, cooking room configuration, season, and burning time per stove were identified as main determinants of PM2.5 exposure.
Our study found that burning wood and other plant materials in unvented stoves resulted in the highest PM2.5 measured both personally and indoors. These measurements are consistent with international research in similar settings for unvented stoves using wood [albeit our measurements are at the lower end of the exposure range (AM for indoor PM2.5 air measurements 520 μg/m3)]. For example, the AM of 22-h PM2.5 measurements of 9 households in kitchens with an open wood stove in Guatemala was 528 ± 249 μg/m3. (24) The geometric mean 48-h PM2.5 of 53 households in kitchens using wood in an open fire without ventilation in rural Mexico was 615 μg/m3, (25) and the AM of 48-h PM2.5 measurements of 63 households in the highlands of San Marcos, Guatemala using open wood fires was 900 ± 700 μg/m3. (26)
PM2.5 exposure levels among coal users in our study were similar to other studies studying coal combustion for cooking and heating in China. An indoor air pollution measurement study in four provinces in China observed indoor Respirable Particulate Matter (RPM) concentrations in the primary biomass fuel provinces of Inner Mongolia and Gansu of 719 and 661 μg/m3, respectively, while indoor RPM concentrations in the primarily coal-burning provinces of Guizhou (202–352 μg/m3) and Shanxi (187–361 μg/m3) were lower. (27) The Sino-Dutch project including 150 households in five counties of three provinces monitored 24-h PM2.5 levels averaging 290 μg/m3 in households mainly burning coal. (28)
The observed differences between studies could be caused by several factors including differences in coal types. In our study, we observed differences in PM2.5 emissions between smoky and smokeless coal (GM for indoor measurements: 144 μg/m3 and 96 μg/m3 respectively). Furthermore, when investigating for variation in PM2.5 measurements between smoky coal sources, we observed significant variation between smoky coal subtypes from Fuyuan. Variation was also observed between coal mines producing the same smoky coal subtypes for the coking, 1/3 coking and gas fat coal subtypes. This indicates that when comparing HAP exposure studies the exact fuel types need to be carefully considered. It also provides indications that the observed health effects may be differential within a particular solid fuel type (e.g., coals). (29)
Many of the previous exposure studies on HAP have focused on measuring indoor air concentrations with relatively few measuring personal exposure, resulting in uncertainty in how these indoor measurements correspond to personal exposure. (20) Several studies have shown that indoor air measurements do not always accurately reflect personal exposure levels to HAP, possibly limiting the interpretation of these indoor measurements. (30, 31) In our study, we found that personal and indoor PM2.5 air measurements were quite similar and that the correlation coefficient between indoor air PM2.5 concentrations and personal PM2.5 exposure levels was high (Spearman r = 0.70, P < 0.0001). This association was modified primarily by the volume of fuel used and temperature (i.e., season) (SI Table S2), both likely proxies of residence time inside the home. This suggests that indoor air concentrations may be good proxies for personal exposure levels of nonsmoking females in this study area. The generalizability of this observation may be limited as our enrollees were generally older women (mean age of 56 years) who were primarily responsible for cooking and housekeeping, meaning that they generally spent the majority of their days indoors. Men, and younger women, who may spend a greater portion of their time outdoors may have different exposure experiences and a weaker correlation between indoor and personal PM2.5 exposure levels.
Mixed effect modeling showed that fuel type, ventilation, number of windows, and season are strong determinants of personal PM2.5 exposure, which reflects some of the findings of the descriptive statistics (specifically fuel types and stove design). Ventilation has been shown to be effective in reducing HAP exposures (24, 25, 30, 32)and in reducing malignant and nonmalignant disease both internationally and in Xuanwei and Fuyuan. We observed a difference of 34–80% in PM2.5 concentrations between vented stoves and unvented stoves/firepits burning smoky coal, wood, plant materials and combinations of coal/plant materials. These values are similar to the reduction reported by the Sino-Dutch project in China where a reduction of 40% was observed after implementation of stove improvements, (33) and slightly lower than what has been reported for stove improvement programs in Latin America where reduction levels ranging between 70% and 80% have been reported among primarily wood burning homes. (24, 25, 30, 32) Additionally, although our study is using a cross-sectional design, our households were selected based on having no stove improvements in the last 5 years. As such the role of stove ventilation presented here represents designs that have been continually used for many years and have likely undergone some “wear and tear”. Therefore, this may result in somewhat lower decreases than observed in other studies that performed their evaluations shortly after the introduction of stove improvements. However, this also shows that stove improvement programs do not only result in a short-term reduction in HAP but one which seems to be sustained for a longer time. The finding of increased personal PM2.5 in the colder seasons is consistent with expected behavior, as people would spend a greater proportion of their time indoors.
The finding that having one window in the main cooking room presented higher modeled PM2.5 measurements than those with zero or two windows seems counterintuitive to the logical expectation. A possible explanation for this observation may relate to home design. If we postulate based on field observations that homes with zero windows represent poorer households, then it is likely that they have poorer construction than homes with windows. This poorer construction may increase home ventilation due to imperfections in the structure of the home acting as ventilation conduits. The role of burning time, standardized by the number of stoves used in personal exposure to PM2.5 is consistent with the expectation of increased exposure with increased burning time.
The findings of this work provide a valuable insight into potential etiological factors of the lung cancer epidemic in Xuanwei and Fuyuan. However, the high PM2.5 measurements in emissions from wood and plant burning homes (which do not have high lung cancer rates when compared to smoky coal burning homes) indicate that measurements of PM2.5 exclusively will not be sufficient to explain the lung cancer epidemic in Xuanwei and Fuyuan. Future research will further investigate the constituents of fuel emissions and work toward associating those emissions with lung cancer epidemiology.

Supporting Information

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Table S1, Variables considered for inclusion in mixed model creation. Table S2, Linear mixed effect model indicating variables which contribute to differences between indoor and personal measurements. Table S3, Linear mixed effect modelling of ln-transformed indoor PM2.5 exposure. This material is available free of charge via the Internet at http://pubs.acs.org.

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Author Information

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  • Corresponding Author
    • Roel Vermeulen - Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht 3508 TD, The Netherlands;  Email: [email protected]
  • Authors
    • Wei Hu - Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
    • George S. Downward - Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht 3508 TD, The Netherlands
    • Boris Reiss - Institute for Risk Assessment Sciences, Division of Environmental Epidemiology, Utrecht University, Utrecht 3508 TD, The Netherlands
    • Jun Xu - Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
    • Bryan A. Bassig - Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
    • H. Dean Hosgood - Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
    • Linlin Zhang - China National Environmental Monitoring Center, Beijing 100029, People’s Republic of China
    • Wei Jie Seow - Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
    • Guoping Wu - China National Environmental Monitoring Center, Beijing 100029, People’s Republic of China
    • Robert S. Chapman - College of Public Health Sciences, Chulalongkorn University, Bangkok 10330, Thailand
    • Linwei Tian - Chinese University of Hong Kong, Hong Kong, People’s Republic of China
    • Fusheng Wei - China National Environmental Monitoring Center, Beijing 100029, People’s Republic of China
    • Qing Lan - Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Service, Rockville, Maryland 20850, United States
  • Notes
    The authors declare no competing financial interest.

Acknowledgment

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This project was supported by the National Institutes of Health intramural research program. The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. The authors would like to acknowledge Jackie King from BioReliance and Peter Hui and Nathan Appel from Information Management Systems, Inc. for their support.

References

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This article references 33 other publications.

  1. 1
    Zhang, J. J.; Smith, K. R. Household air pollution from coal and biomass fuels in China: Measurements, health impacts, and interventions Environ. Health Perspect 2007, 115, 848– 855
    Google Scholar
    1
    Household air pollution from coal and biomass fuels in China: measurements, health impacts, and interventions
    Zhang Junfeng Jim; Smith Kirk R
    Environmental health perspectives (2007), 115 (6), 848-55 ISSN:0091-6765.
    OBJECTIVE: Nearly all China's rural residents and a shrinking fraction of urban residents use solid fuels (biomass and coal) for household cooking and/or heating. Consequently, global meta-analyses of epidemiologic studies indicate that indoor air pollution from solid fuel use in China is responsible for approximately 420,000 premature deaths annually, more than the approximately 300,000 attributed to urban outdoor air pollution in the country. Our objective in this review was to help elucidate the extent of this indoor air pollution health hazard. DATA SOURCES: We reviewed approximately 200 publications in both Chinese- and English-language journals that reported health effects, exposure characteristics, and fuel/stove intervention options. CONCLUSIONS: Observed health effects include respiratory illnesses, lung cancer, chronic obstructive pulmonary disease, weakening of the immune system, and reduction in lung function. Arsenic poisoning and fluorosis resulting from the use of "poisonous" coal have been observed in certain regions of China. Although attempts have been made in a few studies to identify specific coal smoke constituents responsible for specific adverse health effects, the majority of indoor air measurements include those of only particulate matter, carbon monoxide, sulfur dioxide, and/or nitrogen dioxide. These measurements indicate that pollution levels in households using solid fuel generally exceed China's indoor air quality standards. Intervention technologies ranging from simply adding a chimney to the more complex modernized bioenergy program are available, but they can be viable only with coordinated support from the government and the commercial sector.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2sznt1ygsA%253D%253D&md5=8fe8c5567639fbed83cb07225c2c1657
  2. 2
    Hosgood, H. D., III; Menashe, I.; He, X. Z.; Chanock, S.; Lan, Q. PTEN identified as important risk factor of chronic obstructive pulmonary disease Respir. Med. 2009, 103, 1866– 1870
    Google Scholar
    2
    PTEN identified as important risk factor of chronic obstructive pulmonary disease
    Hosgood H Dean 3rd; Menashe Idan; He Xingzhou; Chanock Stephen; Lan Qing
    Respiratory medicine (2009), 103 (12), 1866-70 ISSN:.
    Common genetic variation may play an important role in altering chronic obstructive pulmonary disease (COPD) risk. In Xuanwei, China, the COPD rate is more than twice the Chinese national average, and COPD is strongly associated with in-home coal use. To identify genetic variation that may be associated with COPD in a population with substantial in-home coal smoke exposures, we evaluated 1261 single nucleotide polymorphisms (SNPs) in 380 candidate genes potentially relevant for cancer and other human diseases in a population-based case-control study in Xuanwei (53 cases; 107 controls). PTEN was the most significantly associated gene with COPD in a minP analysis using 20,000 permutations (P=0.00005). SNP-based analyses found that homozygote variant carriers of PTEN rs701848 (OR(TT)=0.12, 95% CI=0.03-0.47) had a significant decreased risk of COPD. PTEN, or phosphatase and tensin homolog, is an important regulator of cell cycle progression and cellular survival via the AKT signaling pathway. Our exploratory analysis suggests that genetic variation in PTEN may be an important risk factor of COPD in Xuanwei. However, due to the small sample size, additional studies are needed to evaluate these associations within Xuanwei and other populations with coal smoke exposures.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1MjkslGmug%253D%253D&md5=8c6d91e11f404d01c84e7ac61a61cffa
  3. 3
    Zhang, J. J.; Hu, W.; Wei, F.; Wu, G.; Korn, L. R.; Chapman, R. S. Children’s respiratory morbidity prevalence in relation to air pollution in four Chinese cities Environ. Health Perspect. 2002, 110, 961– 967
    Google Scholar
    There is no corresponding record for this reference.
  4. 4
    Zhou, X.; Jin, Y.; He, X. Z. A study on the relationship between in-door air pollution and chronic obstructive pulmonary disease in Xuanwei County Zhonghua Yu Fang Yi Xue Za Zhi 1995, 29, 38– 40
    Google Scholar
    4
    A study on the relationship between in-door air pollution and chronic obstructive pulmonary disease in Xuanwei County
    Zhou X; Jin Y; He X
    Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] (1995), 29 (1), 38-40 ISSN:0253-9624.
    A cross-sectional study was conducted to explore the relationship between in-door air pollution caused by different fuels for domestic use and chronic obstructive pulmonary disease (COPD) in local population. A total population of 10892 were sampled and analyzed. Results showed in-door air pollution caused by coal-burning was the major risk factor for COPD in Xuanwei County. The risk of residents using soft coal and anthracite to suffer from COPD was 4.36 and 1.55 times, respectively, higher than in those using fire wood.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADyaK2MzisVaqtg%253D%253D&md5=5c4d8593e23434a0105cdf92ab315ca7
  5. 5
    Ezzati, M., World Health Organization. Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors; World Health Organization; Geneva, 2004.
    Google Scholar
    There is no corresponding record for this reference.
  6. 6
    Chapman, R. S.; He, X. Z.; Blair, A. E.; Lan, Q. Improvement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: Retrospective cohort study Br. Med. J. 2005, 331, 1050
    Google Scholar
    6
    Improvement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: retrospective cohort study
    Chapman Robert S; He Xingzhou; Blair Aaron E; Lan Qing
    BMJ (Clinical research ed.) (2005), 331 (7524), 1050 ISSN:.
    OBJECTIVE: To test whether improvement in household coal stoves affected the incidence of chronic obstructive pulmonary disease (COPD) in Xuanwei County, China. DESIGN: Retrospective cohort study (follow-up 1976-92) comparing incidence of COPD between groups with and without chimneys. PARTICIPANTS: 20,453 people born into homes with unvented coal stoves;16,606 (81.2%) subsequently changed to stoves with chimneys. INTERVENTION: Installation of a chimney in households in which unvented stoves had been used previously. RESULTS: Installation of a chimney was associated with distinct reduction in the incidence of COPD. Compared with people who did not have chimneys, the Cox-modelled risk ratio (relative risk) was 0.58 (95% confidence interval 0.49 to 0.70, P < 0.001) in men and 0.75 (0.62 to 0.92, P = 0.005) in women. Modelled risk ratios were robust to different Cox model specifications. Relative risks decreased with time since stove improvement. In both sexes, the reduction in risk became unequivocal about 10 years after stove improvement. CONCLUSIONS: In Xuanwei, incidence of COPD decreased markedly after household coal stoves were improved.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD2MrptVWltw%253D%253D&md5=634dd89a1879497807b00870ab651f67
  7. 7
    Shen, M.; Vermeulen, R.; Chapman, R. S.; Berndt, S. I.; He, X. Z.; Chanock, S.; Caporaso, N.; Lan, Q. A report of cytokine polymorphisms and COPD risk in Xuan Wei, China Int. J. Hyg. Environ. Health 2008, 211, 352– 356
    Google Scholar
    There is no corresponding record for this reference.
  8. 8
    Smith, K. R.; McCracken, J. P.; Weber, M. W.; Hubbard, A.; Jenny, A.; Thompson, L. M.; Balmes, J.; Diaz, A.; Arana, B.; Bruce, N. Effect of reduction in household air pollution on childhood pneumonia in Guatemala (RESPIRE): A randomised controlled trial Lancet 2011, 378, 1717– 1726
    Google Scholar
    There is no corresponding record for this reference.
  9. 9
    Wu, C.; Hu, Z.; Yu, D.; Huang, L.; Jin, G.; Liang, J.; Guo, H.; Tan, W.; Zhang, M.; Qian, J.; Lu, D.; Wu, T.; Lin, D.; Shen, H. Genetic variants on chromosome 15q25 associated with lung cancer risk in Chinese populations Cancer Res. 2009, 69, 5065– 5072
    Google Scholar
    There is no corresponding record for this reference.
  10. 10
    Shen, M.; Chapman, R. S.; Vermeulen, R.; Tian, L.; Zheng, T.; Chen, B. E.; Engels, E. A.; He, X. Z.; Blair, A.; Lan, Q. Coal use, stove improvement, and adult pneumonia mortality in Xuanwei, China: A retrospective cohort study Environ. Health Perspect. 2009, 117, 261– 266
    Google Scholar
    There is no corresponding record for this reference.
  11. 11
    Mumford, J. L.; He, X. Z.; Chapman, R. S.; Cao, S. R.; Harris, D. B.; Li, X. M.; Xian, Y. L.; Jiang, W. Z.; Xu, C. W.; Chuang, J. C. Lung cancer and indoor air pollution in Xuan Wei, China Science 1987, 235, 217– 220
    Google Scholar
    There is no corresponding record for this reference.
  12. 12
    Lan, Q.; Chapman, R. S.; Schreinemachers, D. M.; Tian, L.; He, X. Z. Household stove improvement and risk of lung cancer in Xuanwei, China J. Natl. Cancer Inst 2002, 94, 826– 835
    Google Scholar
    12
    Household stove improvement and risk of lung cancer in Xuanwei, China
    Lan Qing; Chapman Robert S; Schreinemachers Dina M; Tian Linwei; He Xingzhou
    Journal of the National Cancer Institute (2002), 94 (11), 826-35 ISSN:0027-8874.
    BACKGROUND: Lung cancer rates in rural Xuanwei County, Yunnan Province, are among the highest in China. Residents traditionally burned "smoky" coal in unvented indoor firepits that generated very high levels of air pollution. Since the 1970s, most residents have changed from firepits to stoves with chimneys. This study assessed whether lung cancer incidence decreased after this stove improvement. METHODS: A cohort of 21 232 farmers, born from 1917 through 1951, was followed retrospectively from 1976 through 1992. All subjects were users of smoky coal who had been born into homes with unvented firepits. During their lifetime, 17 184 subjects (80.9%) changed permanently to stoves with chimneys. A hospital record search detected 1384 cases of lung cancer (6.5%) during follow-up. Associations of stove improvement with lung cancer incidence were analyzed with product-limit plots and multivariable Cox models. In 1995, indoor concentrations of airborne particles and benzo[a]pyrene were compared in Xuanwei homes during smoky coal burning in stoves with chimneys and in unvented stoves or firepits. RESULTS: A long-term reduction in lung cancer incidence was noted after stove improvement. In Cox models, risk ratios (RRs) for lung cancer after stove improvement were 0.59 (95% confidence interval [CI] = 0.49 to 0.71) in men and 0.54 (95% CI = 0.44 to 0.65) in women (for both, P<.001). Incidence reduction became unequivocal about 10 years after stove improvement. Levels of indoor air pollution during burning with chimneys were less than 35% of levels during unvented burning. CONCLUSION: Changing from unvented to vented stoves appears to benefit the health of people in China and may do so in other developing countries as well.
    >> More from SciFinder ®
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  13. 13
    He, X. Z.; Chen, W.; Liu, Z. Y.; Chapman, R. S. An epidemiological study of lung cancer in Xuan Wei County, China: current progress. Case-control study on lung cancer and cooking fuel Environ. Health Perspect 1991, 94, 9– 13
    Google Scholar
    There is no corresponding record for this reference.
  14. 14
    Downward, G. S.; Hu, W.; Large, D.; Veld, H.; Xu, J.; Reiss, B.; Wu, G.; Wei, F.; Chapman, R. S.; Rothman, N.; Qing, L.; Vermeulen, R. Heterogeneity in coal composition and implications for lung cancer risk in Xuanwei and Fuyuan counties, China Environ. Int. 2014, 68C, 94– 104
    Google Scholar
    There is no corresponding record for this reference.
  15. 15
    Chen, P. Study on integrated classification system for Chinese coal Fuel Process. Technol. 2000, 62, 77– 87
    Google Scholar
    15
    Study on integrated classification system for Chinese coal
    Chen, P.
    Fuel Processing Technology (2000), 62 (2-3), 77-87CODEN: FPTEDY; ISSN:0378-3820. (Elsevier Science B.V.)
    An integrated coal-classification system in China, i.e., tech./com. and scientific/genetic classification, is discussed. This system shall enable producers, sellers, and purchasers to communicate unambiguously concerning the quality of coal complying with the requirements of the resp. application. The detn. of the perfect coal classification system is important for rational utilization of coal resources.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD3cXhsFagtrc%253D&md5=0da5a5d720aeb508a2bc8364cff5047d
  16. 16
    Lan, Q.; He, X. Z.; Shen, M.; Tian, L.; Liu, L. Z.; Lai, H.; Chen, W.; Berndt, S. I.; Hosgood, H. D.; Lee, K. M.; Zheng, T.; Blair, A.; Chapman, R. S. Variation in lung cancer risk by smoky coal subtype in Xuanwei, China Int. J. Cancer 2008, 123, 2164– 2169
    Google Scholar
    16
    Variation in lung cancer risk by smoky coal subtype in Xuanwei, China
    Lan, Qing; He, Xingzhou; Shen, Min; Tian, Linwei; Liu, Larry Z.; Lai, Hong; Chen, Wei; Berndt, Sonja I.; Hosgood, Howard Dean; Lee, Kyoung-Mu; Zheng, Tongzhang; Blair, Aaron; Chapman, Robert S.
    International Journal of Cancer (2008), 123 (9), 2164-2169CODEN: IJCNAW; ISSN:0020-7136. (Wiley-Liss, Inc.)
    Lung cancer rates in Xuanwei County, among the highest in China for males and females, have been causally assocd. with exposure to indoor smoky (bituminous) coal emissions which contain very high polycyclic arom. hydrocarbon concns. There are numerous coal mines across the county. Although the lung cancer risk is strongly assocd. with the use of smoky coal as a whole, risk variations by smoky coal sub-type have not been characterized. A population-based, case-control study of 498 lung cancer cases and 498 controls was conducted, individually matched to case subjects on age (±2 years) and sex to examine risk by coal sub-type. Odds ratios (OR) and 95% confidence intervals (CI) for coal sub-type were calcd. using conditional logistic regression and adjusting for potential confounders. Overall, smoky coal use was pos., statistically, significantly assocd. with lung cancer risk vs. using smoke-less coal or wood (OR = 7.7, 95% CI = 4.5-13.3). There was a marked heterogeneity in risk ests. for specific smoky coal sub-types (test for heterogeneity: p = 5.17 × 10-10). Ests. were highest for Laibin (OR = 24.8, 95% CI = 12.4-49.6) and Longtan (OR = 11.6, 95% CI = 5.0-27.2) coal types and lower for coal from other sub-types. Results strongly suggested that in Xuanwei and elsewhere, the carcinogenic potential of coal combustion products can exhibit substantial local variation by specific coal source.
    >> More from SciFinder ®
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  17. 17
    Hosgood, H. D., III; Chapman, R.; Shen, M.; Blair, A.; Chen, E.; Zheng, T.; Lee, K. M.; He, X.; Lan, Q. Portable stove use is associated with lower lung cancer mortality risk in lifetime smoky coal users Br. J. Cancer 2008, 99, 1934– 1939
    Google Scholar
    17
    Portable stove use is associated with lower lung cancer mortality risk in lifetime smoky coal users
    Hosgood H D 3rd; Chapman R; Shen M; Blair A; Chen E; Zheng T; Lee K-M; He X; Lan Q
    British journal of cancer (2008), 99 (11), 1934-9 ISSN:.
    Domestic fuel combustion from cooking and heating, to which about 3 billion people worldwide are exposed, is associated with increased lung cancer risk. Lung cancer incidence in Xuanwei is the highest in China, and the attributable risk of lung cancer from unvented smoky coal burning is greater than 90%. To evaluate any lung cancer mortality reduction after changing from unvented stoves to portable stoves, we used lifetime smoky coal users in a retrospective cohort of all farmers born during 1917-1951 and residing in Xuanwei in 1976. Of the 42,422 enrolled farmers, 4054 lifetime smoky coal users changed to portable stoves, 4364 did not change, and 1074 died of lung cancer. Lung cancer morality associated with stove change was assessed by product-limit survival curves and multivariate Cox regression models. Both men (P<0.0001) and women (P<0.0001) who changed to portable stoves had a significantly increased probability of survival compared with those who did not change. Portable stoves were associated with decreased risk of lung cancer mortality in male participants (hazard ratio (HR)=0.62, 95% confidence interval (CI)=0.46-0.82) and female participants (HR=0.41, 95% CI=0.29-0.57). Portable stove use is associated with reduced lung cancer mortality risk, highlighting a cost-effective intervention that could substantially benefit health in developing countries.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BD1cjns1Grtg%253D%253D&md5=b3d697502fd4f2a2f40f4e69bbad89c5
  18. 18
    Barone-Adesi, F.; Chapman, R. S.; Silverman, D. T.; He, X.; Hu, W.; Vermeulen, R.; Ning, B.; Fraumeni, J. F., Jr.; Rothman, N.; Lan, Q. Risk of lung cancer associated with domestic use of coal in Xuanwei, China: Retrospective cohort study Br. Med. J. 2012, 345, e5414
    Google Scholar
    There is no corresponding record for this reference.
  19. 19
    Tian, L. W.; Lan, Q.; Yang, D.; He, X. Z.; Yu, I. T. S.; Hammond, S. K. Effect of chimneys on indoor air concentrations of PM10 and benzo[a]pyrene in Xuan Wei, China Atmos. Environ. 2009, 43, 3352– 3355
    Google Scholar
    There is no corresponding record for this reference.
  20. 20
    Clark, M. L.; Peel, J. L.; Balakrishnan, K.; Breysse, P. N.; Chillrud, S. N.; Naeher, L. P.; Rodes, C. E.; Vette, A. F.; Balbus, J. M. Health and household air pollution from solid fuel use: the need for improved exposure assessment Environ. Health Perspect. 2013, 121, 1120– 1128
    Google Scholar
    There is no corresponding record for this reference.
  21. 21
    Zhou, Z.; Dionisio, K. L.; Verissimo, T. G.; Kerr, A. S.; Coull, B.; Howie, S.; Arku, R. E.; Koutrakis, P.; Spengler, J. D.; Fornace, K.; Hughes, A. F.; Vallarino, J.; Agyei-Mensah, S.; Ezzati, M. Chemical characterization and source apportionment of household fine particulate matter in rural, peri-urban, and urban West Africa Environ. Sci. Technol. 2014, 48, 1343– 1351
    Google Scholar
    There is no corresponding record for this reference.
  22. 22
    Balakrishnan, K.; Ghosh, S.; Ganguli, B.; Sambandam, S.; Bruce, N.; Barnes, D. F.; Smith, K. R. State and national household concentrations of PM2.5 from solid cookfuel use: Results from measurements and modeling in India for estimation of the global burden of disease Environ. Health 2013, 12, 77
    Google Scholar
    22
    State and national household concentrations of PM2.5 from solid cookfuel use: results from measurements and modeling in India for estimation of the global burden of disease
    Balakrishnan, Kalpana; Ghosh, Santu; Ganguli, Bhaswati; Sambandam, Sankar; Bruce, Nigel; Barnes, Douglas F.; Smith, Kirk R.
    Environmental Health (London, United Kingdom) (2013), 12 (), 77/1-77/14CODEN: EHAGAB; ISSN:1476-069X. (BioMed Central Ltd.)
    Background: Previous global burden of disease (GBD) ests. for household air pollution (HAP) from solid cookfuel use were based on categorical indicators of exposure. Recent progress in GBD methodologies that use integrated-exposure-response (IER) curves for combustion particles required the development of models to quant. est. av. HAP levels experienced by large populations. Such models can also serve to inform public health intervention efforts. Thus, we developed a model to est. national household concns. of PM2.5 from solid cookfuel use in India, together with ests. for 29 states. Methods: We monitored 24-h household concns. of PM2.5, in 617 rural households from 4 states in India on a cross-sectional basis between Nov. 2004 and March 2005. We then, developed log-linear regression models that predict household concns. as a function of multiple, independent household level variables available in national household surveys and generated national / state ests. using The Indian National Family and Health Survey (NFHS 2005). Results: The measured mean 24-h concn. of PM2.5 in solid cookfuel using households ranged from 163 μg/m3 (95% CI: 143,183; median 106; IQR: 191) in the living area to 609 μg/m3 (95% CI: 547,671; median: 472; IQR: 734) in the kitchen area. Fuel type, kitchen type, ventilation, geog. location and cooking duration were found to be significant predictors of PM2.5 concns. in the household model. k-fold cross validation showed a fair degree of correlation (r = 0.56) between modeled and measured values. Extrapolation of the household results by state to all solid cookfuel-using households in India, covered by NFHS 2005, resulted in a modeled est. of 450 μg/m3 (95% CI: 318,640) and 113 μg/m3 (95% CI: 102,127) , for national av. 24-h PM2.5 concns. in the kitchen and living areas resp. Conclusions: The model affords substantial improvement over commonly used exposure indicators such as "percent solid cookfuel use" in HAP disease burden assessments, by providing some of the first ests. of national av. HAP levels experienced in India. Model ests. also add considerable strength of evidence for framing and implementation of intervention efforts at the state and national levels.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BC2cXltlyitLg%253D&md5=eaa56b2b6d0f197826d218d47503dc61
  23. 23
    Ge, S.; Xu, X.; Chow, J. C.; Watson, J.; Sheng, Q.; Liu, W.; Bai, Z.; Zhu, T.; Zhang, J. Emissions of air pollutants from household stoves: honeycomb coal versus coal cake Environ. Sci. Technol. 2004, 38, 4612– 4618
    Google Scholar
    There is no corresponding record for this reference.
  24. 24
    Naeher, L. P.; Smith, K. R.; Leaderer, B. P.; Neufeld, L.; Mage, D. T. Carbon monoxide as a tracer for assessing exposures to particulate matter in wood and gas cookstove households of highland Guatemala Environ. Sci. Technol. 2001, 35, 575– 581
    Google Scholar
    There is no corresponding record for this reference.
  25. 25
    Zuk, M.; Rojas, L.; Blanco, S.; Serrano, P.; Cruz, J.; Angeles, F.; Tzintzun, G.; Armendariz, C.; Edwards, R. D.; Johnson, M.; Riojas-Rodriguez, H.; Masera, O. The impact of improved wood-burning stoves on fine particulate matter concentrations in rural Mexican homes J. Expo Sci. Environ. Epidemiol. 2007, 17, 224– 232
    Google Scholar
    25
    The impact of improved wood-burning stoves on fine particulate matter concentrations in rural Mexican homes
    Zuk, Miriam; Rojas, Leonora; Blanco, Salvador; Serrano, Paulina; Cruz, Jephte; Angeles, Felipe; Tzintzun, Guadalupe; Armendariz, Cynthia; Edwards, Rufus D.; Johnson, Michael; Riojas-Rodriguez, Horacio; Masera, Omar
    Journal of Exposure Science & Environmental Epidemiology (2007), 17 (3), 224-232CODEN: JESEBS; ISSN:1559-0631. (Nature Publishing Group)
    To evaluate the impact of improved wood burning stoves on indoor air pollution, 53 homes in a rural town in Michoacan, Mexico, were selected from a health intervention study and monitored before and after receiving improved wood-burning stoves. Fine particulate matter - particles with aerodynamic diam. less than 2.5 μm (PM2.5) - concns. were measured in the central plaza of the community and in three microenvironments in the home (next to the stove, in the kitchen away from the stove, and outdoor patio). Forty-eight hour mean PM2.5 concns. in homes that burned wood in open fires were 693 μg/m3 (95% CI: 246-1338) near the stove, 658 μg/m3 (95% CI: 67-1448) in the kitchen away from the stove, and 94 μg/m3 (95% CI: 36-236) on the patio. Mean ambient 24-h concns. in the main plaza of the community were 59 μg/m3 (95% CI: 29-92). Paired measurements before and after the installation of the Patsari improved wood-burning stove indicate a median 71% redn. in PM2.5 concns. near the stove and 58% redns. in kitchen concns., whereas patio and main plaza concns. remain unaffected. Only 44% of participants reported to use their Patsari stoves exclusively during the transition period. Even with the predominant mixed use of the Patsari stove with open fires, estd. daily av. personal exposures to PM2.5 were reduced by 50%.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A528%3ADC%252BD2sXltlejsLw%253D&md5=f1f268099f909b3bca6b2b186aac3cd1
  26. 26
    Northcross, A.; Chowdhury, Z.; McCracken, J.; Canuz, E.; Smith, K. R. Estimating personal PM2.5 exposures using CO measurements in Guatemalan households cooking with wood fuel J. Environ. Monit. 2010, 12, 873– 878
    Google Scholar
    There is no corresponding record for this reference.
  27. 27
    Jin, Y.; Zhou, Z.; He, G.; Wei, H.; Liu, J.; Liu, F.; Tang, N.; Ying, B.; Liu, Y.; Hu, G.; Wang, H.; Balakrishnan, K.; Watson, K.; Baris, E.; Ezzati, M. Geographical, spatial, and temporal distributions of multiple indoor air pollutants in four Chinese provinces Environ. Sci. Technol. 2005, 39, 9431– 9439
    Google Scholar
    There is no corresponding record for this reference.
  28. 28
    Wei, X. Influence on indoor air quality by using different type of stove and fuel Trans. CSAE 2006, 22, 236– 239
    Google Scholar
    There is no corresponding record for this reference.
  29. 29
    Hosgood, H. D., III; Wei, H.; Sapkota, A.; Choudhury, I.; Bruce, N.; Smith, K. R.; Rothman, N.; Lan, Q. Household coal use and lung cancer: Systematic review and meta-analysis of case-control studies, with an emphasis on geographic variation Int. J. Epidemiol 2011, 40, 719– 728
    Google Scholar
    29
    Household coal use and lung cancer: systematic review and meta-analysis of case-control studies, with an emphasis on geographic variation
    Hosgood H Dean 3rd; Wei Hu; Sapkota Amir; Choudhury Imran; Bruce Nigel; Smith Kirk R; Rothman Nathaniel; Lan Qing
    International journal of epidemiology (2011), 40 (3), 719-28 ISSN:.
    BACKGROUND: Emissions from household coal combustion associated with cooking and heating are an important public health issue, particularly in China where hundreds of millions of people are exposed. Although coal emissions are a known human carcinogen, there is still uncertainty about the level of risk for lung and other cancers. METHODS: We performed a meta-analysis on 25 case-control studies (10,142 cases and 13,416 controls) to summarize the association between household coal use and lung cancer risk, and to explore the effect modification of this association by geographical location. RESULTS: Using random-effects models, household coal use was found to be associated with lung cancer risk among all studies throughout the world [odds ratio (OR) = 2.15; 95% confidence interval (CI) = 1.61-2.89, N(studies) = 25], and particularly among those studies carried out in mainland China and Taiwan (OR = 2.27; 95% CI = 1.65-3.12, N(studies) = 20). Stratification by regions of mainland China and Taiwan found a variation in effects across the regions, with south/southeastern (OR = 3.27; 95% CI = 1.27-8.42, N(studies) = 3) and southwestern China (OR = 2.98; 95% CI = 1.18-7.53, N(studies) = 3) experiencing the highest risk. The elevated risk associated with coal use throughout Asia was also observed when stratifying studies by gender, smoking status, sample size, design (population vs hospital case-control) and publication language. No significant publication bias was found (p(Begg's) = 0.15). CONCLUSIONS: Our results provide evidence that although the carcinogenic effect of coal use varies by location, coals from many locations exhibit elevated lung cancer risks.
    >> More from SciFinder ®
    https://chemport.cas.org/services/resolver?origin=ACS&resolution=options&coi=1%3ACAS%3A280%3ADC%252BC3Mjis12ksw%253D%253D&md5=0cdac84ad21e73a6434c66c4b9a0ed38
  30. 30
    Cynthia, A. A.; Edwards, R. D.; Johnson, M.; Zuk, M.; Rojas, L.; Jimenez, R. D.; Riojas-Rodriguez, H.; Masera, O. Reduction in personal exposures to particulate matter and carbon monoxide as a result of the installation of a Patsari improved cook stove in Michoacan Mexico Indoor Air 2008, 18, 93– 105
    Google Scholar
    There is no corresponding record for this reference.
  31. 31
    Van Vliet, E. D.; Asante, K.; Jack, D. W.; Kinney, P. L.; Whyatt, R. M.; Chillrud, S. N.; Abokyi, L.; Zandoh, C.; Owusu-Agyei, S. Personal exposures to fine particulate matter and black carbon in households cooking with biomass fuels in rural Ghana Environ. Res. 2013, 127, 40– 48
    Google Scholar
    There is no corresponding record for this reference.
  32. 32
    Albalak, R.; Bruce, N.; McCracken, J. P.; Smith, K. R.; De Gallardo, T. Indoor respirable particulate matter concentrations from an open fire, improved cookstove, and LPG/open fire combination in a rural Guatemalan community Environ. Sci. Technol. 2001, 35, 2650– 2655
    Google Scholar
    There is no corresponding record for this reference.
  33. 33
    Dong, R.Assessment of improvements in the Sino-Dutch rural renewable energy project. Chinese Agricultural University report to the Chinese Association of Rural Energy Industries; Chinese Agricultural University; Beijing, China, 2006.
    Google Scholar
    There is no corresponding record for this reference.

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  9. George S. Downward, Wei Hu, Nat Rothman, Boris Reiss, Guoping Wu, Fusheng Wei, Robert S. Chapman, Lutzen Portengen, Lan Qing, and Roel Vermeulen . Polycyclic Aromatic Hydrocarbon Exposure in Household Air Pollution from Solid Fuel Combustion among the Female Population of Xuanwei and Fuyuan Counties, China. Environmental Science & Technology 2014, 48 (24) , 14632-14641. https://doi.org/10.1021/es504102z
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  11. Wei Du, Jie Sun, Jinze Wang, Haitong Zhe Sun, Weijian Liu, Yong Zhang, Nan Lin, Yuanchen Chen, Guofeng Shen. Inhalation exposure to polycyclic aromatic hydrocarbons (PAHs) bound to very fine particles (VFPs): A multi-provincial field investigation in China. Building and Environment 2024, 261 , 111715. https://doi.org/10.1016/j.buildenv.2024.111715
  12. G. Fei, H. Li, S. Yang, H. Wang, Y. Ge, Z. Wang, X. Zhang, P. Wei, L. Li. Burden of lung cancer attributed to particulate matter pollution in China: an epidemiological study from 1990 to 2019. Public Health 2024, 227 , 141-147. https://doi.org/10.1016/j.puhe.2023.12.005
  13. Shuiping Dai, Yuhang He, Ni Zeng, Zhenglu Wang, . Assessment on the Personal Exposure Risk Associated With Real‐Time Indoor PM 2.5 in Different Microenvironments. Indoor Air 2024, 2024 (1) https://doi.org/10.1155/2024/2236778
  14. Batel Blechter, Jason Y Y Wong, Wei Hu, Richard Cawthon, George S Downward, Lützen Portengen, Yongliang Zhang, Bofu Ning, Mohammad L Rahman, Bu-Tian Ji, Jihua Li, Kaiyun Yang, H Dean Hosgood, Debra T Silverman, Yunchao Huang, Nathaniel Rothman, Roel Vermeulen, Qing Lan. Exposure to smoky coal combustion emissions and leukocyte Alu retroelement copy number. Carcinogenesis 2023, 44 (5) , 404-410. https://doi.org/10.1093/carcin/bgad027
  15. Yu-Hsiang Cheng, Meng-Ying Jian, Kuan-Ting Liu, Atar Singh Pipal, Chin-Yu Hsu. Spatial distributions of PM10-bound metal elements in the central part of western Taiwan and their potential emission sources and the carcinogenic health risks. Environmental Science and Pollution Research 2023, 30 (38) , 88495-88507. https://doi.org/10.1007/s11356-023-28675-7
  16. Shanshan Shi, Junling Yang, Yushu Liang. Indoor distribution and personal exposure of cooking-generated PM2.5 in rural residences of China: A multizone model study. Building Simulation 2023, 16 (8) , 1299-1315. https://doi.org/10.1007/s12273-023-0997-1
  17. Batel Blechter, Andres Cardenas, Junming Shi, Jason Y.Y. Wong, Wei Hu, Mohammad L. Rahman, Charles Breeze, George S. Downward, Lützen Portengen, Yongliang Zhang, Bofu Ning, Bu-Tian Ji, Richard Cawthon, Jihua Li, Kaiyun Yang, Anne Bozack, H. Dean Hosgood, Debra T. Silverman, Yunchao Huang, Nathaniel Rothman, Roel Vermeulen, Qing Lan. Household air pollution and epigenetic aging in Xuanwei, China. Environment International 2023, 178 , 108041. https://doi.org/10.1016/j.envint.2023.108041
  18. Jianbang Xiang, Linmin Hu, Jiayuan Hao, Siqing Wu, Jianping Cao, Edmund Seto. Characterization of time- and size-dependent particle emissions and decay from cooking oil fumes in residence: Impacts of various intervention measures. Building Simulation 2023, 16 (7) , 1149-1158. https://doi.org/10.1007/s12273-023-1016-2
  19. Dong Sun, Cong Liu, Yunqing Zhu, Canqing Yu, Yu Guo, Dianjianyi Sun, Yuanjie Pang, Pei Pei, Huaidong Du, Ling Yang, Yiping Chen, Xia Meng, Yang Liu, Jun Zhang, Dan Schmidt, Daniel Avery, Junshi Chen, Zhengming Chen, Jun Lv, Haidong Kan, Liming Li, Junshi Chen, Zhengming Chen, Robert Clarke, Rory Collins, Yu Guo, Liming Li, Richard Peto, Robin Walters, Daniel Avery, Derrick Bennett, Ruth Boxall, Sue Burgess, Ka Hung Chan, Yumei Chang, Yiping Chen, Johnathan Clarke, Huaidong Du, Ahmed Edris Mohamed, Zammy Fairhurst-Hunter, Hannah Fry, Simon Gilbert, Alex Hacker, Mike Hill, Michael Holmes, Pek Kei Im, Andri Iona, Maria Kakkoura, Christiana Kartsonaki, Rene Kerosi, Kuang Lin, Mohsen Mazidi, Iona Millwood, Sam Morris, Qunhua Nie, Alfred Pozarickij, Paul Ryder, Saredo Said, Sam Sansome, Dan Schmidt, Paul Sherliker, Rajani Sohoni, Becky Stevens, Iain Turnbull, Lin Wang, Neil Wright, Ling Yang, Xiaoming Yang, Pang Yao, Xiao Han, Can Hou, Jun Lv, Pei Pei, Chao Liu, Canqing Yu, Qingmei Xia, Zengchang Pang, Ruqin Gao, Shanpeng Li, Haiping Duan, Shaojie Wang, Yongmei Liu, Ranran Du, Yajing Zang, Liang Cheng, Xiaocao Tian, Hua Zhang, Yaoming Zhai, Feng Ning, Xiaohui Sun, Feifei Li, Silu Lv, Junzheng Wang, Wei Hou, Wei Sun, Shichun Yan, Xiaoming Cui, Chi Wang, Zhenyuan Wu, Yanjie Li, Quan Kang, Huiming Luo, Tingting Ou, Xiangyang Zheng, Zhendong Guo, Shukuan Wu, Yilei Li, Huimei Li, Ming Wu, Yonglin Zhou, Jinyi Zhou, Ran Tao, Jie Yang, Jian Su, Fang Liu, Jun Zhang, Yihe Hu, Yan Lu, Liangcai Ma, Aiyu Tang, Shuo Zhang, Jianrong Jin, Jingchao Liu, Mei Lin, Zhenzhen Lu, Lifang Zhou, Changping Xie, Jian Lan, Tingping Zhu, Yun Liu, Liuping Wei, Liyuan Zhou, Ningyu Chen, Yulu Qin, Sisi Wang, Xianping Wu, Ningmei Zhang, Xiaofang Chen, Xiaoyu Chang, Mingqiang Yuan, Xia Wu, Wei Jiang, Jiaqiu Liu, Qiang Sun, Faqing Chen, Xiaolan Ren, Caixia Dong, Hui Zhang, Enke Mao, Xiaoping Wang, Tao Wang, Xi Zhang, Kai Kang, Shixian Feng, Huizi Tian, Lei Fan, XiaoLin Li, Huarong Sun, Pan He, Xukui Zhang, Min Yu, Ruying Hu, Hao Wang, Xiaoyi Zhang, Yuan Cao, Kaixu Xie, Lingli Chen, Dun Shen, Xiaojun Li, Donghui Jin, Li Yin, Huilin Liu, Zhongxi Fu, Xin Xu, Hao Zhang, Jianwei Chen, Yuan Peng, Libo Zhang, Chan Qu. Long-Term Exposure to Fine Particulate Matter and Incidence of Esophageal Cancer: A Prospective Study of 0.5 Million Chinese Adults. Gastroenterology 2023, 165 (1) , 61-70.e5. https://doi.org/10.1053/j.gastro.2023.03.233
  20. Yuzhe Liu, Di Wu, Jinrun Yi, Xiu Chen, Yuankai Shao, Anlin Liu, Jianmin Chen, Qing Li. Unequal Health Risks of Integrated Volatile Organic Compounds Emitted From Various Anthropogenic Sources. Journal of Geophysical Research: Atmospheres 2023, 128 (10) https://doi.org/10.1029/2023JD038594
  21. Lützen Portengen, George Downward, Bryan A. Bassig, Batel Blechter, Wei Hu, Jason Y.Y. Wong, Bofu Ning, Mohammad L. Rahman, Bu-Tian Ji, Jihua Li, Kaiyun Yang, H. Dean Hosgood, Debra T. Silverman, Nathaniel Rothman, Yunchao Huang, Roel Vermeulen, Qing Lan. Methylated polycyclic aromatic hydrocarbons from household coal use across the life course and risk of lung cancer in a large cohort of 42,420 subjects in Xuanwei, China. Environment International 2023, 173 , 107870. https://doi.org/10.1016/j.envint.2023.107870
  22. Bin Liang, Haolong Bai, Liangliang Fu, Dingrong Bai. Characteristics of the particulate matter and its toxic substances from different stationary coal-fired sources. Fuel 2023, 334 , 126594. https://doi.org/10.1016/j.fuel.2022.126594
  23. Ning Li, Xianwei Guo, Yuyan Wu, Vicky Yau, Qiuxia Song, Wanying Su, Hao Wang, Qiwei Liang, Mingming Liang, Xiuxiu Ding, Scott Lowe, Yaru Li, Rachel Bentley, Bethany King, Qin Zhou, Guangbo Qu, Chenyu Sun. Association between household air pollution and all-cause and cause-specific mortality: a systematic review and meta-analysis. Environmental Science and Pollution Research 2023, 30 (3) , 5312-5346. https://doi.org/10.1007/s11356-022-24222-y
  24. Weijian Liu, Wei Du, Jinze Wang, Shaojie Zhuo, Yuanchen Chen, Nan Lin, Guorui Kong, Bo Pan. PAHs bound to submicron particles in rural Chinese homes burning solid fuels. Ecotoxicology and Environmental Safety 2022, 247 , 114274. https://doi.org/10.1016/j.ecoenv.2022.114274
  25. Caiyun Zhang, Ning Kang, Xiaoyu Hou, Gaohua Chang, Yinghao Yuchi, Xiaotian Liu, Wenqian Huo, Zhenxing Mao, Jian Hou, Chongjian Wang. The Effect of Kitchen Ventilation Modification on Independent and Combined Associations of Cooking Fuel Type and Cooking Duration with Suicidal Ideation: A Cross-Sectional Study. Toxics 2022, 10 (12) , 721. https://doi.org/10.3390/toxics10120721
  26. Mulako D. Mukelabai, K. G. U. Wijayantha, Richard E. Blanchard. Hydrogen for Cooking: A Review of Cooking Technologies, Renewable Hydrogen Systems and Techno-Economics. Sustainability 2022, 14 (24) , 16964. https://doi.org/10.3390/su142416964
  27. Teja Nagaradona, Bryan A Bassig, Dean Hosgood, Roel C H Vermeulen, Bofu Ning, Wei Jie Seow, Wei Hu, Lützen Portengen, Jason Wong, Xiao-Ou Shu, Wei Zheng, Nathan Appel, Yu-Tang Gao, Qiu-Yin Cai, Gong Yang, Ying Chen, George Downward, Jihua Li, Kaiyun Yang, Lauren McCullough, Debra Silverman, Yunchao Huang, Qing Lan. Overall and cause-specific mortality rates among men and women with high exposure to indoor air pollution from the use of smoky and smokeless coal: a cohort study in Xuanwei, China. BMJ Open 2022, 12 (11) , e058714. https://doi.org/10.1136/bmjopen-2021-058714
  28. Elvin S. Cheng, Ka Hung Chan, Marianne Weber, Julia Steinberg, Jane Young, Karen Canfell, Xue Qin Yu. Solid Fuel, Secondhand Smoke, and Lung Cancer Mortality: A Prospective Cohort of 323,794 Chinese Never-Smokers. American Journal of Respiratory and Critical Care Medicine 2022, 206 (9) , 1153-1162. https://doi.org/10.1164/rccm.202201-0114OC
  29. Yushan Huang, Huiling Wang, Huizhen Guo, Yang Liu, Yong Zeng, Fengjing Hu, Wenwen Zhang, Chengyun Li, Junling Wang. A preliminary study on household air pollution exposure and health-related factors among rural housewives in Gansu province, northwest China. Archives of Environmental & Occupational Health 2022, 77 (8) , 662-673. https://doi.org/10.1080/19338244.2021.1993775
  30. Rongshou Zheng, Siwei Zhang, Shaoming Wang, Ru Chen, Kexin Sun, Hongmei Zeng, Li Li, Wenqiang Wei, Jie He. Lung cancer incidence and mortality in China: Updated statistics and an overview of temporal trends from 2000 to 2016. Journal of the National Cancer Center 2022, 2 (3) , 139-147. https://doi.org/10.1016/j.jncc.2022.07.004
  31. Bangeppagari Manjunatha, Eunseok Seo, Deekshitha Bangyappagari, Sang Joon Lee. Histopathological and ultrastructural alterations reveal the toxicity of particulate matter (PM2.5) in adult zebrafish. Journal of Hazardous Materials Advances 2022, 7 , 100135. https://doi.org/10.1016/j.hazadv.2022.100135
  32. Yue Peng, Yu Wang, Fei Wu, Yongjie Chen. Association of cooking fuel with incident hypertension among adults in China: A population‐based cohort study. The Journal of Clinical Hypertension 2022, 24 (8) , 1003-1011. https://doi.org/10.1111/jch.14533
  33. Xiaoyu Hou, Zhenxing Mao, Xiaoqin Song, Ning Kang, Caiyun Zhang, Ruiying Li, Yinghao Yuchi, Wei Liao, Xiaotian Liu, Wenqian Huo, Chongjian Wang, Jian Hou. Kitchen ventilation alleviated adverse associations of domestic fuel use and long-duration cooking with platelet indices as biomarkers of cardiovascular diseases. Science of The Total Environment 2022, 834 , 155341. https://doi.org/10.1016/j.scitotenv.2022.155341
  34. Anna Kerpel-Fronius, Zsuzsanna Monostori, Gabor Kovacs, Gyula Ostoros, Istvan Horvath, Diana Solymosi, Orsolya Pipek, Ferenc Szatmari, Anita Kovacs, Zsolt Markoczy, Livia Rojko, Ferenc Renyi-Vamos, Konrad Hoetzenecker, Krisztina Bogos, Zsolt Megyesfalvi, Balazs Dome. Nationwide lung cancer screening with low-dose computed tomography: implementation and first results of the HUNCHEST screening program. European Radiology 2022, 32 (7) , 4457-4467. https://doi.org/10.1007/s00330-022-08589-7
  35. Iu-Jin Lee, Sang-Gil Lee, Bo-Hwa Choi, Hoe-Kyeong Seo, Ji-Hyung Choi. Hazard Levels of Cooking Fumes in Republic of Korea Schools. Safety and Health at Work 2022, 13 (2) , 227-234. https://doi.org/10.1016/j.shaw.2021.12.702
  36. Chenchen Zhang, Hejiang Sun. Analysis and Modeling of Mechanical Ventilation Operation Behaviors of Occupants in Cold Regions of North China. Applied Sciences 2022, 12 (10) , 5143. https://doi.org/10.3390/app12105143
  37. Dongsheng Ji, Jiawei Li, Guofeng Shen, Jun He, Wenkang Gao, Jun Tao, Yu Liu, Guiqian Tang, Limin Zeng, Renjian Zhang, Yuesi Wang. Environmental effects of China's coal ban policy: Results from in situ observations and model analysis in a typical rural area of the Beijing-Tianjin-Hebei region, China. Atmospheric Research 2022, 268 , 106015. https://doi.org/10.1016/j.atmosres.2022.106015
  38. Ying Wang, Matthew Shupler, Aaron Birch, Yen Li Chu, Matthew Jeronimo, Sumathy Rangarajan, Maha Mustaha, Laura Heenan, Pamela Seron, Fernando Lanas, Luis Salazar, Nicolas Saavedra, Maria Jose Oliveros, Patricio Lopez-Jaramillo, Paul A. Camacho, Johnna Otero, Maritza Perez-Mayorga, Karen Yeates, Nicola West, Tatenda Ncube, Brian Ncube, Jephat Chifamba, Rita Yusuf, Afreen Khan, Zhiguang Liu, Hu Bo, Li Wei, L.A. Tse, Deepa Mohan, Parthiban Kumar, Rajeev Gupta, Indu Mohan, K.G. Jayachitra, Prem K. Mony, Kamala Rammohan, Sanjeev Nair, P.V.M. Lakshmi, Vivek Sagar, Rehman Khawaja, Romaina Iqbal, Khawar Kazmi, Salim Yusuf, Michael Brauer, Perry Hystad. Measuring and predicting personal and household Black Carbon levels from 88 communities in eight countries. Science of The Total Environment 2022, 818 , 151849. https://doi.org/10.1016/j.scitotenv.2021.151849
  39. Juan Qi, Lianxin Liu, Jianjun Wu. Improving Combustion Technology for Cooking Activities for Pollutant Emission Reduction and Carbon Neutrality. Atmosphere 2022, 13 (4) , 561. https://doi.org/10.3390/atmos13040561
  40. Wen Sun, Weidong Yan, Kailiang Huang, Jian Ma, Shu Liu, Guoqi Liu, , , . Analysis of the effect of air purification equipment on indoor pollutants in Northeast China in winter. E3S Web of Conferences 2022, 356 , 05066. https://doi.org/10.1051/e3sconf/202235605066
  41. Yungui Li, Yuqiong Wang, Jinze Wang, Long Chen, Zhenglu Wang, Sheng Feng, Nan Lin, Wei Du. Quantify individual variation of real‐time PM 2.5 exposure in urban Chinese homes based on a novel method. Indoor Air 2022, 32 (1) https://doi.org/10.1111/ina.12962
  42. Johannes Dirk Dingemanse, Geertje Dingemanse-de Wit. An Evaluation of Best Practices in an Air Quality Student Science Project in Ethiopia. Aquademia 2022, 6 (1) , ep22001. https://doi.org/10.21601/aquademia/11828
  43. Joshua P. Keller, Maggie L. Clark. Estimating long-term average household air pollution concentrations from repeated short-term measurements in the presence of seasonal trends and crossover. Environmental Epidemiology 2022, 6 (1) , e188. https://doi.org/10.1097/EE9.0000000000000188
  44. Yongliang Zhang, Kees Meliefste, Wei Hu, Jihua Li, Jun Xu, Baofu Ning, Kaiyun Yang, Ying Chen, Dingyu Liu, Jason Wong, Mohammad Rahman, Nathaniel Rothman, Yunchao Huang, Flemming Cassee, Roel Vermeulen, Qing Lan, George S. Downward. Household air pollution from, and fuel efficiency of, different coal types following local cooking practices in Xuanwei, China. Environmental Pollution 2021, 290 , 117949. https://doi.org/10.1016/j.envpol.2021.117949
  45. Donghyun Kim, Hyerin Shin, Sooyoung Guak, Jargalsaikhan Galsuren, Davaalkham Dambadarjaa, Kiyoung Lee. Personal PM2.5 exposures of husband and wife by residential characteristics in Ulaanbaatar, Mongolia. Air Quality, Atmosphere & Health 2021, 14 (11) , 1849-1856. https://doi.org/10.1007/s11869-021-01060-4
  46. Sara Mohamed, Lucelia Rodrigues, Siddig Omer, John Calautit. Overheating and indoor air quality in primary schools in the UK. Energy and Buildings 2021, 250 , 111291. https://doi.org/10.1016/j.enbuild.2021.111291
  47. Steven J. Hadeed, Mary Kay O’Rourke, Robert A. Canales, Lorencita Joshweseoma, Gregory Sehongva, Morris Paukgana, Emmanuel Gonzalez‐Figueroa, Modhi Alshammari, Jefferey L. Burgess, Robin B. Harris. Household and behavioral determinants of indoor PM 2.5 in a rural solid fuel burning Native American community. Indoor Air 2021, 31 (6) , 2008-2019. https://doi.org/10.1111/ina.12904
  48. Jason Y. Y. Wong, Roel Vermeulen, Yufei Dai, Wei Hu, W. Kyle Martin, Sarah H. Warren, Hannah K. Liberatore, Dianzhi Ren, Huawei Duan, Yong Niu, Jun Xu, Wei Fu, Kees Meliefste, Jufang Yang, Meng Ye, Xiaowei Jia, Tao Meng, Bryan A. Bassig, H. Dean Hosgood, Jiyeon Choi, Mohammad L. Rahman, Douglas I. Walker, Yuxin Zheng, Judy Mumford, Debra T. Silverman, Nathaniel Rothman, David M. DeMarini, Qing Lan. Elevated urinary mutagenicity among those exposed to bituminous coal combustion emissions or diesel engine exhaust. Environmental and Molecular Mutagenesis 2021, 62 (8) , 458-470. https://doi.org/10.1002/em.22455
  49. Phuong T.M. Tran, Max G. Adam, Kwok Wai Tham, Stefano Schiavon, Jovan Pantelic, Paul F. Linden, Eleni Sofianopoulou, S. Chandra Sekhar, David Kok Wai Cheong, Rajasekhar Balasubramanian. Assessment and mitigation of personal exposure to particulate air pollution in cities: An exploratory study. Sustainable Cities and Society 2021, 72 , 103052. https://doi.org/10.1016/j.scs.2021.103052
  50. Yu‐yan Yang, Lin Fan, Jiao Wang, Yuan‐duo Zhu, Xu Li, Xin‐qi Wang, Xu Yan, Li Li, Yu‐jing Zhang, Wen‐jing Yang, Xiao‐yuan Yao, Xian‐liang Wang. Characterization and exposure assessment of household fine particulate matter pollution in China. Indoor Air 2021, 31 (5) , 1391-1401. https://doi.org/10.1111/ina.12843
  51. Yuanyuan Song, Lifang Zhao, Zenghua Qi, Yanhao Zhang, Guodong Cao, Ruijin Li, Lin Zhu, Zhu Yang, Chuan Dong, Zongwei Cai. Application of a real-ambient fine particulate matter exposure system on different animal models. Journal of Environmental Sciences 2021, 105 , 64-70. https://doi.org/10.1016/j.jes.2020.12.007
  52. Wei Du, Jinze Wang, Shanshan Zhang, Nan Fu, Fengqin Yang, Gehui Wang, Zhenglu Wang, Kang Mao, Guofeng Shen, Meng Qi, Shijie Liu, Can Wu, Yuanchen Chen. Impacts of Chinese spring festival on household PM 2.5 pollution and blood pressure of rural residents. Indoor Air 2021, 31 (4) , 1072-1083. https://doi.org/10.1111/ina.12795
  53. Chao-Lang Kao, Guor-Cheng Fang, Wei-Shun Gao, Yuan-Jie Zhuang. Concentrations, sizes distributions, and seasonal variations of ambient air pollutants (particulates, trace metals) in Daya/Xitun District, Taichung, Central Taiwan: a case study at Taichung Science Park. Journal of Environmental Science and Health, Part A 2021, 56 (7) , 824-834. https://doi.org/10.1080/10934529.2021.1936988
  54. Dujia Wang, Xiaoru Yuan, Jianbo Jia, Shuai He, Xiaoxia Zhou, Bing Yan. Al3+ reduces PM2.5-induced cytotoxicity in human bronchial epithelial cells via reducing ROS production. Air Quality, Atmosphere & Health 2021, 14 (6) , 903-909. https://doi.org/10.1007/s11869-021-00988-x
  55. Wanying Chen, Yiang Chen, Yeqi Huang, Xingcheng Lu, Jian Zhen Yu, Jimmy C.H. Fung, Changqing Lin, Yulong Yan, Lin Peng, Peter K.K. Louie, Frankie C.V. Tam, Dingli Yue, Alexis K.H. Lau, Liuju Zhong. Source apportionment of fine secondary inorganic aerosol over the Pearl River Delta region using a hybrid method. Atmospheric Pollution Research 2021, 12 (5) , 101061. https://doi.org/10.1016/j.apr.2021.101061
  56. Ning Xu, Fan Zhang, Xin Xuan. Impacts of Industrial Restructuring and Technological Progress on PM2.5 Pollution: Evidence from Prefecture-Level Cities in China. International Journal of Environmental Research and Public Health 2021, 18 (10) , 5283. https://doi.org/10.3390/ijerph18105283
  57. Ye Huang, Jinze Wang, Nan Fu, Shanshan Zhang, Wei Du, YuanChen Chen, Zhenglu Wang, Meng Qi, Wei Wang, Qirui Zhong, Yonghong Duan, Guofeng Shen, Shu Tao. Inhalation exposure to size-segregated fine particles and particulate PAHs for the population burning biomass fuels in the Eastern Tibetan Plateau area. Ecotoxicology and Environmental Safety 2021, 211 , 111959. https://doi.org/10.1016/j.ecoenv.2021.111959
  58. Yanhao Zhang, Yi-Jie Chen, Yuanyuan Song, Chuan Dong, Zongwei Cai. Atmospheric pressure gas chromatography-tandem mass spectrometry analysis of fourteen emerging polycyclic aromatic sulfur heterocycles in PM2.5. Chinese Chemical Letters 2021, 32 (2) , 801-804. https://doi.org/10.1016/j.cclet.2020.07.022
  59. Xiaotao He, Guangqiang Zhou, Yueling Ma, Lanyu Li, Shihua Fu, Shang Liu, Ce Liu, Yonglin He, Zheng Su, Jiangtao Liu, Jingping Niu, Weihao Zhu, Kai Zhang, Bin Luo. Winter vacation, indoor air pollution and respiratory health among rural college students: A case study in Gansu Province, China. Building and Environment 2021, 188 , 107481. https://doi.org/10.1016/j.buildenv.2020.107481
  60. Ka Hung Chan, Xi Xia, Kin-fai Ho, Yu Guo, Om P Kurmi, Huaidong Du, Derrick A Bennett, Zheng Bian, Haidong Kan, John McDonnell, Dan Schmidt, Rene Kerosi, Liming Li, Kin Bong Hubert Lam, Zhengming Chen. Regional and seasonal variations in household and personal exposures to air pollution in one urban and two rural Chinese communities: A pilot study to collect time-resolved data using static and wearable devices. Environment International 2021, 146 , 106217. https://doi.org/10.1016/j.envint.2020.106217
  61. Xiaoyu Hou, Zhenxing Mao, Xiaoqin Song, Ning Kang, Caiyun Zhang, Ruiying Li, Yinghao Yuchi, Wei Liao, Xiaotian Liu, Wenqian Huo, Chongjian Wang, Jian Hou. Kitchen Ventilation Alleviated Adverse Associations of Domestic Fuel Use and Long-Duration Cooking with Platelet Indices as Biomarkers of Cardiovascular Diseases. SSRN Electronic Journal 2021, 76 https://doi.org/10.2139/ssrn.3994595
  62. Phuong T.M. Tran, Teron Nguyen, Rajasekhar Balasubramanian. Personal exposure to airborne particles in transport micro-environments and potential health impacts: A tale of two cities. Sustainable Cities and Society 2020, 63 , 102470. https://doi.org/10.1016/j.scs.2020.102470
  63. Kun Wang, Weiye Wang, Lili Li, Jianju Li, Liangliang Wei, Wanqiu Chi, Lijing Hong, Qingliang Zhao, Junqiu Jiang. Seasonal concentration distribution of PM1.0 and PM2.5 and a risk assessment of bound trace metals in Harbin, China: Effect of the species distribution of heavy metals and heat supply. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-65187-7
  64. Wei Hu, George Downward, Jason Y. Y. Wong, Boris Reiss, Nathaniel Rothman, Lützen Portengen, Jihua Li, Rena R. Jones, Yunchao Huang, Kaiyun Yang, Ying Chen, Jun Xu, Jun He, Bryan Bassig, Wei Jie Seow, H. Dean Hosgood, Linlin Zhang, Guoping Wu, Fusheng Wei, Roel Vermeulen, Qing Lan. Characterization of outdoor air pollution from solid fuel combustion in Xuanwei and Fuyuan, a rural region of China. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-68229-2
  65. Runxiang Yang, Ming He, Dongmei Wang, Rongrong Ye, Lu Li, Rouyu Deng, Mohsin Shah, Sai-Ching Jim Yeung. Association of cancer screening and residing in a coal-polluted East Asian region with overall survival of lung cancer patients: a retrospective cohort study. Scientific Reports 2020, 10 (1) https://doi.org/10.1038/s41598-020-74082-0
  66. Jianxin Li, Xiangfeng Lu, Fangchao Liu, Fengchao Liang, Keyong Huang, Xueli Yang, Qingyang Xiao, Jichun Chen, Xiaoqing Liu, Jie Cao, Shufeng Chen, Chong Shen, Ling Yu, Fanghong Lu, Xianping Wu, Liancheng Zhao, Xigui Wu, Ying Li, Dongsheng Hu, Jianfeng Huang, Meng Zhu, Yang Liu, Hongbing Shen, Dongfeng Gu. Chronic Effects of High Fine Particulate Matter Exposure on Lung Cancer in China. American Journal of Respiratory and Critical Care Medicine 2020, 202 (11) , 1551-1559. https://doi.org/10.1164/rccm.202001-0002OC
  67. Ge Mu, Bin Wang, Shijie Yang, Xing Wang, Min Zhou, Wenjing Song, Weihong Qiu, Zi Ye, Yun Zhou, Weihong Chen. Assessment for personal PM2.5 exposure with a modeling method: A panel study in Wuhan, China. Atmospheric Pollution Research 2020, 11 (11) , 1991-1997. https://doi.org/10.1016/j.apr.2020.08.016
  68. Raveen Fatima, Shazia Iram. A model of eco-friendly cooking stove and a potential application of soot for remediation of heavy metals in the environment. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 2020, 4 , 1-17. https://doi.org/10.1080/15567036.2020.1827087
  69. Yuanchen Chen, Jie Fei, Zhe Sun, Guofeng Shen, Wei Du, Lu Zang, Liyang Yang, Yonghui Wang, Ruxin Wu, An Chen, Meirong Zhao. Household air pollution from cooking and heating and its impacts on blood pressure in residents living in rural cave dwellings in Loess Plateau of China. Environmental Science and Pollution Research 2020, 27 (29) , 36677-36687. https://doi.org/10.1007/s11356-020-09677-1
  70. Tao Wang, Haitao Wang, Jian Chen, Jiansheng Wang, Dunqiang Ren, Wei Hu, Hongmei Wang, Wei Han, Shuguang Leng, Rong Zhang, Yuxin Zheng. Association between air pollution and lung development in schoolchildren in China. Journal of Epidemiology and Community Health 2020, 74 (10) , 792-798. https://doi.org/10.1136/jech-2020-214283
  71. Khalid Mehmood, Saifullah, Muhammad Iqbal, Zed Rengel, Muhammad Mohsin Abrar. Pakistan and India Collaboration to Improve Regional Air Quality Has Never Been More Promising. Integrated Environmental Assessment and Management 2020, 16 (5) , 549-551. https://doi.org/10.1002/ieam.4292
  72. Shuyin Duan, Meihua Zhang, Yaqiong Sun, Zhenya Fang, Hefeng Wang, Shuxian Li, Yanze Peng, Juan Li, Junxia Li, Jiaqi Tian, Haoyu Yin, Sanqiao Yao, Lin Zhang. Mechanism of PM2.5-induced human bronchial epithelial cell toxicity in central China. Journal of Hazardous Materials 2020, 396 , 122747. https://doi.org/10.1016/j.jhazmat.2020.122747
  73. Xiaoyu Wang, Xuejing Cui, Yuliang Zhao, Chunying Chen. Nano-bio interactions: the implication of size-dependent biological effects of nanomaterials. Science China Life Sciences 2020, 63 (8) , 1168-1182. https://doi.org/10.1007/s11427-020-1725-0
  74. Chang Xu, Liang Dong, Chang Yu, Yingjie Zhang, Baodong Cheng. Can forest city construction affect urban air quality? The evidence from the Beijing-Tianjin-Hebei urban agglomeration of China. Journal of Cleaner Production 2020, 264 , 121607. https://doi.org/10.1016/j.jclepro.2020.121607
  75. Tanxin Liu, Yongfeng Song, Ru Chen, Rongshou Zheng, Shengfeng Wang, Liming Li. Solid fuel use for heating and risks of breast and cervical cancer mortality in China. Environmental Research 2020, 186 , 109578. https://doi.org/10.1016/j.envres.2020.109578
  76. Xing Zheng, Tong Liu, Menghan Guo, Dong Li, Na Gou, Xin Cao, Xiaopeng Qiu, Xiaoliang Li, Yaozhong Zhang, Guoping Sheng, Baozhu Pan, April Z. Gu, Zhanbin Li. Impact of heavy metals on the formation and properties of solvable microbiological products released from activated sludge in biological wastewater treatment. Water Research 2020, 179 , 115895. https://doi.org/10.1016/j.watres.2020.115895
  77. Lanyu Li, Aiming Yang, Xiaotao He, Jiangtao Liu, Yueling Ma, Jingping Niu, Bin Luo. Indoor air pollution from solid fuels and hypertension: A systematic review and meta-analysis. Environmental Pollution 2020, 259 , 113914. https://doi.org/10.1016/j.envpol.2020.113914
  78. Bryan A Bassig, H Dean Hosgood, Xiao-Ou Shu, Roel Vermeulen, Bingshu E Chen, Hormuzd A Katki, Wei Jie Seow, Wei Hu, Lützen Portengen, Bu-Tian Ji, Jason Y Y Wong, Bofu Ning, George S Downward, Jihua Li, Kaiyun Yang, Gong Yang, Yu-Tang Gao, Yong-Bing Xiang, Teja Nagaradona, Wei Zheng, Debra T Silverman, Yunchao Huang, Qing Lan. Ischaemic heart disease and stroke mortality by specific coal type among non-smoking women with substantial indoor air pollution exposure in China. International Journal of Epidemiology 2020, 49 (1) , 56-68. https://doi.org/10.1093/ije/dyz158
  79. Otavio T Ranzani, Carles Milà, Margaux Sanchez, Santhi Bhogadi, Bharati Kulkarni, Kalpana Balakrishnan, Sankar Sambandam, Jordi Sunyer, Julian D Marshall, Sanjay Kinra, Cathryn Tonne. Association between ambient and household air pollution with carotid intima-media thickness in peri-urban South India: CHAI-Project. International Journal of Epidemiology 2020, 49 (1) , 69-79. https://doi.org/10.1093/ije/dyz208
  80. Otavio T. Ranzani, Carles Milà, Bharati Kulkarni, Sanjay Kinra, Cathryn Tonne. Association of Ambient and Household Air Pollution With Bone Mineral Content Among Adults in Peri-urban South India. JAMA Network Open 2020, 3 (1) , e1918504. https://doi.org/10.1001/jamanetworkopen.2019.18504
  81. Xin Zhang. Particles Matter, Nitrogen Dioxide and Sulfur Dioxide. 2020, 217-235. https://doi.org/10.1007/978-981-32-9182-9_11
  82. Sumal Nandasena, A. R. Wickremasinghe, Nalini Sathiakumar. Combustion and Cookstoves. 2020, 237-250. https://doi.org/10.1007/978-981-32-9182-9_12
  83. Jie Wu, Xia Xiao, Yan Li, Fan Yang, Siwei Yang, Lin Sun, Rui Ma, May C. Wang. Personal exposure to fine particulate matter (PM2.5) of pregnant women during three trimesters in rural Yunnan of China. Environmental Pollution 2020, 256 , 113055. https://doi.org/10.1016/j.envpol.2019.113055
  84. Wei Yu, Lexiang Wang, Qingqin Wang, Xiaofei Wang, Guozhu Li, Junliang Wang, Hazim Awbi. Design selection and evaluation method of PM2.5 filters for fresh air systems. Journal of Building Engineering 2020, 27 , 100977. https://doi.org/10.1016/j.jobe.2019.100977
  85. Robert J. Moriarity, Meaghan J. Wilton, Eric N. Liberda, Leonard J.S. Tsuji, Richard E. Peltier. Wood smoke black carbon from Indigenous traditional cultural activities in a subarctic Cree community. International Journal of Circumpolar Health 2020, 79 (1) https://doi.org/10.1080/22423982.2020.1811517
  86. Xilei Dai, Junjie Liu, Xin Zhang. Monte Carlo simulation to control indoor pollutants from indoor and outdoor sources for residential buildings in Tianjin, China. Building and Environment 2019, 165 , 106376. https://doi.org/10.1016/j.buildenv.2019.106376
  87. Ariadna Curto, David Donaire-Gonzalez, Maria N. Manaca, Raquel González, Charfudin Sacoor, Ioar Rivas, Mireia Gascon, Gregory A. Wellenius, Xavier Querol, Jordi Sunyer, Eusébio Macete, Clara Menéndez, Cathryn Tonne. Predictors of personal exposure to black carbon among women in southern semi-rural Mozambique. Environment International 2019, 131 , 104962. https://doi.org/10.1016/j.envint.2019.104962
  88. Jing Li, Haoxuan Chen, Xinyue Li, Minfei Wang, Xiangyu Zhang, Junji Cao, Fangxia Shen, Yan Wu, Siyu Xu, Hanqing Fan, Guillaume Da, Ru-jin Huang, Jing Wang, Chak K. Chan, Alma Lorelei De Jesus, Lidia Morawska, Maosheng Yao. Differing toxicity of ambient particulate matter (PM) in global cities. Atmospheric Environment 2019, 212 , 305-315. https://doi.org/10.1016/j.atmosenv.2019.05.048
  89. Roel Vermeulen, George S. Downward, Jinming Zhang, Wei Hu, Lützen Portengen, Bryan A. Bassig, S. Katharine Hammond, Jason Y.Y. Wong, Jihua Li, Boris Reiss, Jun He, Linwei Tian, Kaiyun Yang, Wei Jie Seow, Jun Xu, Kim Anderson, Bu-Tian Ji, Debra Silverman, Stephen Chanock, Yunchao Huang, Nathaniel Rothman, Qing Lan. Constituents of Household Air Pollution and Risk of Lung Cancer among Never-Smoking Women in Xuanwei and Fuyuan, China. Environmental Health Perspectives 2019, 127 (9) https://doi.org/10.1289/EHP4913
  90. Wonhwa Lee, So Yeon Jeong, Myeong Ju Gu, Ji Sun Lim, Eui Kyun Park, Moon-Chang Baek, Jong-Sang Kim, Dongyup Hahn, Jong-Sup Bae. Inhibitory effects of compounds isolated from Dioscorea batatas Decne peel on particulate matter-induced pulmonary injury in mice. Journal of Toxicology and Environmental Health, Part A 2019, 82 (12) , 727-740. https://doi.org/10.1080/15287394.2019.1646174
  91. Jason Y.Y. Wong, George S. Downward, Wei Hu, Lützen Portengen, Wei Jie Seow, Debra T. Silverman, Bryan A. Bassig, Jinming Zhang, Jun Xu, Bu‐Tian Ji, Jihua Li, Jun He, Kaiyun Yang, Linwei Tian, Min Shen, Yunchao Huang, Roel Vermeulen, Nathaniel Rothman, Qing Lan. Lung cancer risk by geologic coal deposits: A case–control study of female never‐smokers from Xuanwei and Fuyuan, China. International Journal of Cancer 2019, 144 (12) , 2918-2927. https://doi.org/10.1002/ijc.32034
  92. C. D. Dong, S. C. C. Lung, C. W. Chen, J. S. Lee, Y. C. Chen, W. C. V. Wang, C. J. Chen, C. M. Hung, C. H. Lin. Assessment of the pulmonary toxic potential of nano-tobacco stem-pyrolyzed biochars. Environmental Science: Nano 2019, 6 (5) , 1527-1535. https://doi.org/10.1039/C8EN00968F
  93. Ruolan Hu, Shuxiao Wang, Kristin Aunan, Minjiang Zhao, Lu Chen, Zhaohui Liu, Mette H. Hansen. Personal exposure to PM 2.5 in Chinese rural households in the Yangtze River Delta. Indoor Air 2019, 29 (3) , 403-412. https://doi.org/10.1111/ina.12537
  94. Yanhao Zhang, Yanyan Chen, Ruijin Li, Wei Chen, Yuanyuan Song, Di Hu, Zongwei Cai. Determination of PM2.5-bound polyaromatic hydrocarbons and their hydroxylated derivatives by atmospheric pressure gas chromatography-tandem mass spectrometry. Talanta 2019, 195 , 757-763. https://doi.org/10.1016/j.talanta.2018.12.006
  95. Jason Y.Y. Wong, Bryan A. Bassig, Wei Hu, Wei Jie Seow, Meredith S. Shiels, Bu-Tian Ji, George S. Downward, Yunchao Huang, Kaiyun Yang, Jihua Li, Jun He, Ying Chen, Allan Hildesheim, Roel Vermeulen, Qing Lan, Nathaniel Rothman. Household coal combustion, indoor air pollutants, and circulating immunologic/inflammatory markers in rural China. Journal of Toxicology and Environmental Health, Part A 2019, 82 (6) , 411-421. https://doi.org/10.1080/15287394.2019.1614500
  96. Jinhui Li, Jinjun Ran, Lung-chi Chen, Max Costa, Yunchao Huang, Xiao Chen, Linwei Tian. Bituminous coal combustion and Xuan Wei Lung cancer: a review of the epidemiology, intervention, carcinogens, and carcinogenesis. Archives of Toxicology 2019, 93 (3) , 573-583. https://doi.org/10.1007/s00204-019-02392-y
  97. Meng Qi, Wei Du, Xi Zhu, Wei Wang, Cengxi Lu, Yuanchen Chen, Guofeng Shen, Hefa Cheng, Eddy Y. Zeng, Shu Tao. Fluctuation in time-resolved PM2.5 from rural households with solid fuel-associated internal emission sources. Environmental Pollution 2019, 244 , 304-313. https://doi.org/10.1016/j.envpol.2018.10.041
  98. Asmi Panigrahi, Bijaya K. Padhi. Chronic bronchitis and airflow obstruction is associated with household cooking fuel use among never-smoking women: a community-based cross-sectional study in Odisha, India. BMC Public Health 2018, 18 (1) https://doi.org/10.1186/s12889-018-5846-2
  99. Kristin Aunan, Qiao Ma, Marianne T. Lund, Shuxiao Wang. Population-weighted exposure to PM2.5 pollution in China: An integrated approach. Environment International 2018, 120 , 111-120. https://doi.org/10.1016/j.envint.2018.07.042
  100. Miyoung Lim, Sainnyambuu Myagmarchuluun, Hyunkyung Ban, Yunhyung Hwang, Chimedsuren Ochir, Delgerzul Lodoisamba, Kiyoung Lee. Characteristics of Indoor PM2.5 Concentration in Gers Using Coal Stoves in Ulaanbaatar, Mongolia. International Journal of Environmental Research and Public Health 2018, 15 (11) , 2524. https://doi.org/10.3390/ijerph15112524
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Cite this: Environ. Sci. Technol. 2014, 48, 15, 8456–8464
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Published July 8, 2014

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  • Abstract

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    Figure 1. Villages selected for the exposure survey and coal mines in Xuanwei and Fuyuan counties. Classification of coal regions based on the State Standard of China Coal Classification (GB5751–86); 1/3 coking, coking, gas fat, and meager lean coals are subtypes of smoky coal.

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  • References

    This article references 33 other publications.

    1. 1
      Zhang, J. J.; Smith, K. R. Household air pollution from coal and biomass fuels in China: Measurements, health impacts, and interventions Environ. Health Perspect 2007, 115, 848– 855
      1
      Household air pollution from coal and biomass fuels in China: measurements, health impacts, and interventions
      Zhang Junfeng Jim; Smith Kirk R
      Environmental health perspectives (2007), 115 (6), 848-55 ISSN:0091-6765.
      OBJECTIVE: Nearly all China's rural residents and a shrinking fraction of urban residents use solid fuels (biomass and coal) for household cooking and/or heating. Consequently, global meta-analyses of epidemiologic studies indicate that indoor air pollution from solid fuel use in China is responsible for approximately 420,000 premature deaths annually, more than the approximately 300,000 attributed to urban outdoor air pollution in the country. Our objective in this review was to help elucidate the extent of this indoor air pollution health hazard. DATA SOURCES: We reviewed approximately 200 publications in both Chinese- and English-language journals that reported health effects, exposure characteristics, and fuel/stove intervention options. CONCLUSIONS: Observed health effects include respiratory illnesses, lung cancer, chronic obstructive pulmonary disease, weakening of the immune system, and reduction in lung function. Arsenic poisoning and fluorosis resulting from the use of "poisonous" coal have been observed in certain regions of China. Although attempts have been made in a few studies to identify specific coal smoke constituents responsible for specific adverse health effects, the majority of indoor air measurements include those of only particulate matter, carbon monoxide, sulfur dioxide, and/or nitrogen dioxide. These measurements indicate that pollution levels in households using solid fuel generally exceed China's indoor air quality standards. Intervention technologies ranging from simply adding a chimney to the more complex modernized bioenergy program are available, but they can be viable only with coordinated support from the government and the commercial sector.
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    2. 2
      Hosgood, H. D., III; Menashe, I.; He, X. Z.; Chanock, S.; Lan, Q. PTEN identified as important risk factor of chronic obstructive pulmonary disease Respir. Med. 2009, 103, 1866– 1870
      2
      PTEN identified as important risk factor of chronic obstructive pulmonary disease
      Hosgood H Dean 3rd; Menashe Idan; He Xingzhou; Chanock Stephen; Lan Qing
      Respiratory medicine (2009), 103 (12), 1866-70 ISSN:.
      Common genetic variation may play an important role in altering chronic obstructive pulmonary disease (COPD) risk. In Xuanwei, China, the COPD rate is more than twice the Chinese national average, and COPD is strongly associated with in-home coal use. To identify genetic variation that may be associated with COPD in a population with substantial in-home coal smoke exposures, we evaluated 1261 single nucleotide polymorphisms (SNPs) in 380 candidate genes potentially relevant for cancer and other human diseases in a population-based case-control study in Xuanwei (53 cases; 107 controls). PTEN was the most significantly associated gene with COPD in a minP analysis using 20,000 permutations (P=0.00005). SNP-based analyses found that homozygote variant carriers of PTEN rs701848 (OR(TT)=0.12, 95% CI=0.03-0.47) had a significant decreased risk of COPD. PTEN, or phosphatase and tensin homolog, is an important regulator of cell cycle progression and cellular survival via the AKT signaling pathway. Our exploratory analysis suggests that genetic variation in PTEN may be an important risk factor of COPD in Xuanwei. However, due to the small sample size, additional studies are needed to evaluate these associations within Xuanwei and other populations with coal smoke exposures.
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    3. 3
      Zhang, J. J.; Hu, W.; Wei, F.; Wu, G.; Korn, L. R.; Chapman, R. S. Children’s respiratory morbidity prevalence in relation to air pollution in four Chinese cities Environ. Health Perspect. 2002, 110, 961– 967
      There is no corresponding record for this reference.
    4. 4
      Zhou, X.; Jin, Y.; He, X. Z. A study on the relationship between in-door air pollution and chronic obstructive pulmonary disease in Xuanwei County Zhonghua Yu Fang Yi Xue Za Zhi 1995, 29, 38– 40
      4
      A study on the relationship between in-door air pollution and chronic obstructive pulmonary disease in Xuanwei County
      Zhou X; Jin Y; He X
      Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine] (1995), 29 (1), 38-40 ISSN:0253-9624.
      A cross-sectional study was conducted to explore the relationship between in-door air pollution caused by different fuels for domestic use and chronic obstructive pulmonary disease (COPD) in local population. A total population of 10892 were sampled and analyzed. Results showed in-door air pollution caused by coal-burning was the major risk factor for COPD in Xuanwei County. The risk of residents using soft coal and anthracite to suffer from COPD was 4.36 and 1.55 times, respectively, higher than in those using fire wood.
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    5. 5
      Ezzati, M., World Health Organization. Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors; World Health Organization; Geneva, 2004.
      There is no corresponding record for this reference.
    6. 6
      Chapman, R. S.; He, X. Z.; Blair, A. E.; Lan, Q. Improvement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: Retrospective cohort study Br. Med. J. 2005, 331, 1050
      6
      Improvement in household stoves and risk of chronic obstructive pulmonary disease in Xuanwei, China: retrospective cohort study
      Chapman Robert S; He Xingzhou; Blair Aaron E; Lan Qing
      BMJ (Clinical research ed.) (2005), 331 (7524), 1050 ISSN:.
      OBJECTIVE: To test whether improvement in household coal stoves affected the incidence of chronic obstructive pulmonary disease (COPD) in Xuanwei County, China. DESIGN: Retrospective cohort study (follow-up 1976-92) comparing incidence of COPD between groups with and without chimneys. PARTICIPANTS: 20,453 people born into homes with unvented coal stoves;16,606 (81.2%) subsequently changed to stoves with chimneys. INTERVENTION: Installation of a chimney in households in which unvented stoves had been used previously. RESULTS: Installation of a chimney was associated with distinct reduction in the incidence of COPD. Compared with people who did not have chimneys, the Cox-modelled risk ratio (relative risk) was 0.58 (95% confidence interval 0.49 to 0.70, P < 0.001) in men and 0.75 (0.62 to 0.92, P = 0.005) in women. Modelled risk ratios were robust to different Cox model specifications. Relative risks decreased with time since stove improvement. In both sexes, the reduction in risk became unequivocal about 10 years after stove improvement. CONCLUSIONS: In Xuanwei, incidence of COPD decreased markedly after household coal stoves were improved.
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    7. 7
      Shen, M.; Vermeulen, R.; Chapman, R. S.; Berndt, S. I.; He, X. Z.; Chanock, S.; Caporaso, N.; Lan, Q. A report of cytokine polymorphisms and COPD risk in Xuan Wei, China Int. J. Hyg. Environ. Health 2008, 211, 352– 356
      There is no corresponding record for this reference.
    8. 8
      Smith, K. R.; McCracken, J. P.; Weber, M. W.; Hubbard, A.; Jenny, A.; Thompson, L. M.; Balmes, J.; Diaz, A.; Arana, B.; Bruce, N. Effect of reduction in household air pollution on childhood pneumonia in Guatemala (RESPIRE): A randomised controlled trial Lancet 2011, 378, 1717– 1726
      There is no corresponding record for this reference.
    9. 9
      Wu, C.; Hu, Z.; Yu, D.; Huang, L.; Jin, G.; Liang, J.; Guo, H.; Tan, W.; Zhang, M.; Qian, J.; Lu, D.; Wu, T.; Lin, D.; Shen, H. Genetic variants on chromosome 15q25 associated with lung cancer risk in Chinese populations Cancer Res. 2009, 69, 5065– 5072
      There is no corresponding record for this reference.
    10. 10
      Shen, M.; Chapman, R. S.; Vermeulen, R.; Tian, L.; Zheng, T.; Chen, B. E.; Engels, E. A.; He, X. Z.; Blair, A.; Lan, Q. Coal use, stove improvement, and adult pneumonia mortality in Xuanwei, China: A retrospective cohort study Environ. Health Perspect. 2009, 117, 261– 266
      There is no corresponding record for this reference.
    11. 11
      Mumford, J. L.; He, X. Z.; Chapman, R. S.; Cao, S. R.; Harris, D. B.; Li, X. M.; Xian, Y. L.; Jiang, W. Z.; Xu, C. W.; Chuang, J. C. Lung cancer and indoor air pollution in Xuan Wei, China Science 1987, 235, 217– 220
      There is no corresponding record for this reference.
    12. 12
      Lan, Q.; Chapman, R. S.; Schreinemachers, D. M.; Tian, L.; He, X. Z. Household stove improvement and risk of lung cancer in Xuanwei, China J. Natl. Cancer Inst 2002, 94, 826– 835
      12
      Household stove improvement and risk of lung cancer in Xuanwei, China
      Lan Qing; Chapman Robert S; Schreinemachers Dina M; Tian Linwei; He Xingzhou
      Journal of the National Cancer Institute (2002), 94 (11), 826-35 ISSN:0027-8874.
      BACKGROUND: Lung cancer rates in rural Xuanwei County, Yunnan Province, are among the highest in China. Residents traditionally burned "smoky" coal in unvented indoor firepits that generated very high levels of air pollution. Since the 1970s, most residents have changed from firepits to stoves with chimneys. This study assessed whether lung cancer incidence decreased after this stove improvement. METHODS: A cohort of 21 232 farmers, born from 1917 through 1951, was followed retrospectively from 1976 through 1992. All subjects were users of smoky coal who had been born into homes with unvented firepits. During their lifetime, 17 184 subjects (80.9%) changed permanently to stoves with chimneys. A hospital record search detected 1384 cases of lung cancer (6.5%) during follow-up. Associations of stove improvement with lung cancer incidence were analyzed with product-limit plots and multivariable Cox models. In 1995, indoor concentrations of airborne particles and benzo[a]pyrene were compared in Xuanwei homes during smoky coal burning in stoves with chimneys and in unvented stoves or firepits. RESULTS: A long-term reduction in lung cancer incidence was noted after stove improvement. In Cox models, risk ratios (RRs) for lung cancer after stove improvement were 0.59 (95% confidence interval [CI] = 0.49 to 0.71) in men and 0.54 (95% CI = 0.44 to 0.65) in women (for both, P<.001). Incidence reduction became unequivocal about 10 years after stove improvement. Levels of indoor air pollution during burning with chimneys were less than 35% of levels during unvented burning. CONCLUSION: Changing from unvented to vented stoves appears to benefit the health of people in China and may do so in other developing countries as well.
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    13. 13
      He, X. Z.; Chen, W.; Liu, Z. Y.; Chapman, R. S. An epidemiological study of lung cancer in Xuan Wei County, China: current progress. Case-control study on lung cancer and cooking fuel Environ. Health Perspect 1991, 94, 9– 13
      There is no corresponding record for this reference.
    14. 14
      Downward, G. S.; Hu, W.; Large, D.; Veld, H.; Xu, J.; Reiss, B.; Wu, G.; Wei, F.; Chapman, R. S.; Rothman, N.; Qing, L.; Vermeulen, R. Heterogeneity in coal composition and implications for lung cancer risk in Xuanwei and Fuyuan counties, China Environ. Int. 2014, 68C, 94– 104
      There is no corresponding record for this reference.
    15. 15
      Chen, P. Study on integrated classification system for Chinese coal Fuel Process. Technol. 2000, 62, 77– 87
      15
      Study on integrated classification system for Chinese coal
      Chen, P.
      Fuel Processing Technology (2000), 62 (2-3), 77-87CODEN: FPTEDY; ISSN:0378-3820. (Elsevier Science B.V.)
      An integrated coal-classification system in China, i.e., tech./com. and scientific/genetic classification, is discussed. This system shall enable producers, sellers, and purchasers to communicate unambiguously concerning the quality of coal complying with the requirements of the resp. application. The detn. of the perfect coal classification system is important for rational utilization of coal resources.
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    16. 16
      Lan, Q.; He, X. Z.; Shen, M.; Tian, L.; Liu, L. Z.; Lai, H.; Chen, W.; Berndt, S. I.; Hosgood, H. D.; Lee, K. M.; Zheng, T.; Blair, A.; Chapman, R. S. Variation in lung cancer risk by smoky coal subtype in Xuanwei, China Int. J. Cancer 2008, 123, 2164– 2169
      16
      Variation in lung cancer risk by smoky coal subtype in Xuanwei, China
      Lan, Qing; He, Xingzhou; Shen, Min; Tian, Linwei; Liu, Larry Z.; Lai, Hong; Chen, Wei; Berndt, Sonja I.; Hosgood, Howard Dean; Lee, Kyoung-Mu; Zheng, Tongzhang; Blair, Aaron; Chapman, Robert S.
      International Journal of Cancer (2008), 123 (9), 2164-2169CODEN: IJCNAW; ISSN:0020-7136. (Wiley-Liss, Inc.)
      Lung cancer rates in Xuanwei County, among the highest in China for males and females, have been causally assocd. with exposure to indoor smoky (bituminous) coal emissions which contain very high polycyclic arom. hydrocarbon concns. There are numerous coal mines across the county. Although the lung cancer risk is strongly assocd. with the use of smoky coal as a whole, risk variations by smoky coal sub-type have not been characterized. A population-based, case-control study of 498 lung cancer cases and 498 controls was conducted, individually matched to case subjects on age (±2 years) and sex to examine risk by coal sub-type. Odds ratios (OR) and 95% confidence intervals (CI) for coal sub-type were calcd. using conditional logistic regression and adjusting for potential confounders. Overall, smoky coal use was pos., statistically, significantly assocd. with lung cancer risk vs. using smoke-less coal or wood (OR = 7.7, 95% CI = 4.5-13.3). There was a marked heterogeneity in risk ests. for specific smoky coal sub-types (test for heterogeneity: p = 5.17 × 10-10). Ests. were highest for Laibin (OR = 24.8, 95% CI = 12.4-49.6) and Longtan (OR = 11.6, 95% CI = 5.0-27.2) coal types and lower for coal from other sub-types. Results strongly suggested that in Xuanwei and elsewhere, the carcinogenic potential of coal combustion products can exhibit substantial local variation by specific coal source.
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    17. 17
      Hosgood, H. D., III; Chapman, R.; Shen, M.; Blair, A.; Chen, E.; Zheng, T.; Lee, K. M.; He, X.; Lan, Q. Portable stove use is associated with lower lung cancer mortality risk in lifetime smoky coal users Br. J. Cancer 2008, 99, 1934– 1939
      17
      Portable stove use is associated with lower lung cancer mortality risk in lifetime smoky coal users
      Hosgood H D 3rd; Chapman R; Shen M; Blair A; Chen E; Zheng T; Lee K-M; He X; Lan Q
      British journal of cancer (2008), 99 (11), 1934-9 ISSN:.
      Domestic fuel combustion from cooking and heating, to which about 3 billion people worldwide are exposed, is associated with increased lung cancer risk. Lung cancer incidence in Xuanwei is the highest in China, and the attributable risk of lung cancer from unvented smoky coal burning is greater than 90%. To evaluate any lung cancer mortality reduction after changing from unvented stoves to portable stoves, we used lifetime smoky coal users in a retrospective cohort of all farmers born during 1917-1951 and residing in Xuanwei in 1976. Of the 42,422 enrolled farmers, 4054 lifetime smoky coal users changed to portable stoves, 4364 did not change, and 1074 died of lung cancer. Lung cancer morality associated with stove change was assessed by product-limit survival curves and multivariate Cox regression models. Both men (P<0.0001) and women (P<0.0001) who changed to portable stoves had a significantly increased probability of survival compared with those who did not change. Portable stoves were associated with decreased risk of lung cancer mortality in male participants (hazard ratio (HR)=0.62, 95% confidence interval (CI)=0.46-0.82) and female participants (HR=0.41, 95% CI=0.29-0.57). Portable stove use is associated with reduced lung cancer mortality risk, highlighting a cost-effective intervention that could substantially benefit health in developing countries.
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    18. 18
      Barone-Adesi, F.; Chapman, R. S.; Silverman, D. T.; He, X.; Hu, W.; Vermeulen, R.; Ning, B.; Fraumeni, J. F., Jr.; Rothman, N.; Lan, Q. Risk of lung cancer associated with domestic use of coal in Xuanwei, China: Retrospective cohort study Br. Med. J. 2012, 345, e5414
      There is no corresponding record for this reference.
    19. 19
      Tian, L. W.; Lan, Q.; Yang, D.; He, X. Z.; Yu, I. T. S.; Hammond, S. K. Effect of chimneys on indoor air concentrations of PM10 and benzo[a]pyrene in Xuan Wei, China Atmos. Environ. 2009, 43, 3352– 3355
      There is no corresponding record for this reference.
    20. 20
      Clark, M. L.; Peel, J. L.; Balakrishnan, K.; Breysse, P. N.; Chillrud, S. N.; Naeher, L. P.; Rodes, C. E.; Vette, A. F.; Balbus, J. M. Health and household air pollution from solid fuel use: the need for improved exposure assessment Environ. Health Perspect. 2013, 121, 1120– 1128
      There is no corresponding record for this reference.
    21. 21
      Zhou, Z.; Dionisio, K. L.; Verissimo, T. G.; Kerr, A. S.; Coull, B.; Howie, S.; Arku, R. E.; Koutrakis, P.; Spengler, J. D.; Fornace, K.; Hughes, A. F.; Vallarino, J.; Agyei-Mensah, S.; Ezzati, M. Chemical characterization and source apportionment of household fine particulate matter in rural, peri-urban, and urban West Africa Environ. Sci. Technol. 2014, 48, 1343– 1351
      There is no corresponding record for this reference.
    22. 22
      Balakrishnan, K.; Ghosh, S.; Ganguli, B.; Sambandam, S.; Bruce, N.; Barnes, D. F.; Smith, K. R. State and national household concentrations of PM2.5 from solid cookfuel use: Results from measurements and modeling in India for estimation of the global burden of disease Environ. Health 2013, 12, 77
      22
      State and national household concentrations of PM2.5 from solid cookfuel use: results from measurements and modeling in India for estimation of the global burden of disease
      Balakrishnan, Kalpana; Ghosh, Santu; Ganguli, Bhaswati; Sambandam, Sankar; Bruce, Nigel; Barnes, Douglas F.; Smith, Kirk R.
      Environmental Health (London, United Kingdom) (2013), 12 (), 77/1-77/14CODEN: EHAGAB; ISSN:1476-069X. (BioMed Central Ltd.)
      Background: Previous global burden of disease (GBD) ests. for household air pollution (HAP) from solid cookfuel use were based on categorical indicators of exposure. Recent progress in GBD methodologies that use integrated-exposure-response (IER) curves for combustion particles required the development of models to quant. est. av. HAP levels experienced by large populations. Such models can also serve to inform public health intervention efforts. Thus, we developed a model to est. national household concns. of PM2.5 from solid cookfuel use in India, together with ests. for 29 states. Methods: We monitored 24-h household concns. of PM2.5, in 617 rural households from 4 states in India on a cross-sectional basis between Nov. 2004 and March 2005. We then, developed log-linear regression models that predict household concns. as a function of multiple, independent household level variables available in national household surveys and generated national / state ests. using The Indian National Family and Health Survey (NFHS 2005). Results: The measured mean 24-h concn. of PM2.5 in solid cookfuel using households ranged from 163 μg/m3 (95% CI: 143,183; median 106; IQR: 191) in the living area to 609 μg/m3 (95% CI: 547,671; median: 472; IQR: 734) in the kitchen area. Fuel type, kitchen type, ventilation, geog. location and cooking duration were found to be significant predictors of PM2.5 concns. in the household model. k-fold cross validation showed a fair degree of correlation (r = 0.56) between modeled and measured values. Extrapolation of the household results by state to all solid cookfuel-using households in India, covered by NFHS 2005, resulted in a modeled est. of 450 μg/m3 (95% CI: 318,640) and 113 μg/m3 (95% CI: 102,127) , for national av. 24-h PM2.5 concns. in the kitchen and living areas resp. Conclusions: The model affords substantial improvement over commonly used exposure indicators such as "percent solid cookfuel use" in HAP disease burden assessments, by providing some of the first ests. of national av. HAP levels experienced in India. Model ests. also add considerable strength of evidence for framing and implementation of intervention efforts at the state and national levels.
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    23. 23
      Ge, S.; Xu, X.; Chow, J. C.; Watson, J.; Sheng, Q.; Liu, W.; Bai, Z.; Zhu, T.; Zhang, J. Emissions of air pollutants from household stoves: honeycomb coal versus coal cake Environ. Sci. Technol. 2004, 38, 4612– 4618
      There is no corresponding record for this reference.
    24. 24
      Naeher, L. P.; Smith, K. R.; Leaderer, B. P.; Neufeld, L.; Mage, D. T. Carbon monoxide as a tracer for assessing exposures to particulate matter in wood and gas cookstove households of highland Guatemala Environ. Sci. Technol. 2001, 35, 575– 581
      There is no corresponding record for this reference.
    25. 25
      Zuk, M.; Rojas, L.; Blanco, S.; Serrano, P.; Cruz, J.; Angeles, F.; Tzintzun, G.; Armendariz, C.; Edwards, R. D.; Johnson, M.; Riojas-Rodriguez, H.; Masera, O. The impact of improved wood-burning stoves on fine particulate matter concentrations in rural Mexican homes J. Expo Sci. Environ. Epidemiol. 2007, 17, 224– 232
      25
      The impact of improved wood-burning stoves on fine particulate matter concentrations in rural Mexican homes
      Zuk, Miriam; Rojas, Leonora; Blanco, Salvador; Serrano, Paulina; Cruz, Jephte; Angeles, Felipe; Tzintzun, Guadalupe; Armendariz, Cynthia; Edwards, Rufus D.; Johnson, Michael; Riojas-Rodriguez, Horacio; Masera, Omar
      Journal of Exposure Science & Environmental Epidemiology (2007), 17 (3), 224-232CODEN: JESEBS; ISSN:1559-0631. (Nature Publishing Group)
      To evaluate the impact of improved wood burning stoves on indoor air pollution, 53 homes in a rural town in Michoacan, Mexico, were selected from a health intervention study and monitored before and after receiving improved wood-burning stoves. Fine particulate matter - particles with aerodynamic diam. less than 2.5 μm (PM2.5) - concns. were measured in the central plaza of the community and in three microenvironments in the home (next to the stove, in the kitchen away from the stove, and outdoor patio). Forty-eight hour mean PM2.5 concns. in homes that burned wood in open fires were 693 μg/m3 (95% CI: 246-1338) near the stove, 658 μg/m3 (95% CI: 67-1448) in the kitchen away from the stove, and 94 μg/m3 (95% CI: 36-236) on the patio. Mean ambient 24-h concns. in the main plaza of the community were 59 μg/m3 (95% CI: 29-92). Paired measurements before and after the installation of the Patsari improved wood-burning stove indicate a median 71% redn. in PM2.5 concns. near the stove and 58% redns. in kitchen concns., whereas patio and main plaza concns. remain unaffected. Only 44% of participants reported to use their Patsari stoves exclusively during the transition period. Even with the predominant mixed use of the Patsari stove with open fires, estd. daily av. personal exposures to PM2.5 were reduced by 50%.
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    26. 26
      Northcross, A.; Chowdhury, Z.; McCracken, J.; Canuz, E.; Smith, K. R. Estimating personal PM2.5 exposures using CO measurements in Guatemalan households cooking with wood fuel J. Environ. Monit. 2010, 12, 873– 878
      There is no corresponding record for this reference.
    27. 27
      Jin, Y.; Zhou, Z.; He, G.; Wei, H.; Liu, J.; Liu, F.; Tang, N.; Ying, B.; Liu, Y.; Hu, G.; Wang, H.; Balakrishnan, K.; Watson, K.; Baris, E.; Ezzati, M. Geographical, spatial, and temporal distributions of multiple indoor air pollutants in four Chinese provinces Environ. Sci. Technol. 2005, 39, 9431– 9439
      There is no corresponding record for this reference.
    28. 28
      Wei, X. Influence on indoor air quality by using different type of stove and fuel Trans. CSAE 2006, 22, 236– 239
      There is no corresponding record for this reference.
    29. 29
      Hosgood, H. D., III; Wei, H.; Sapkota, A.; Choudhury, I.; Bruce, N.; Smith, K. R.; Rothman, N.; Lan, Q. Household coal use and lung cancer: Systematic review and meta-analysis of case-control studies, with an emphasis on geographic variation Int. J. Epidemiol 2011, 40, 719– 728
      29
      Household coal use and lung cancer: systematic review and meta-analysis of case-control studies, with an emphasis on geographic variation
      Hosgood H Dean 3rd; Wei Hu; Sapkota Amir; Choudhury Imran; Bruce Nigel; Smith Kirk R; Rothman Nathaniel; Lan Qing
      International journal of epidemiology (2011), 40 (3), 719-28 ISSN:.
      BACKGROUND: Emissions from household coal combustion associated with cooking and heating are an important public health issue, particularly in China where hundreds of millions of people are exposed. Although coal emissions are a known human carcinogen, there is still uncertainty about the level of risk for lung and other cancers. METHODS: We performed a meta-analysis on 25 case-control studies (10,142 cases and 13,416 controls) to summarize the association between household coal use and lung cancer risk, and to explore the effect modification of this association by geographical location. RESULTS: Using random-effects models, household coal use was found to be associated with lung cancer risk among all studies throughout the world [odds ratio (OR) = 2.15; 95% confidence interval (CI) = 1.61-2.89, N(studies) = 25], and particularly among those studies carried out in mainland China and Taiwan (OR = 2.27; 95% CI = 1.65-3.12, N(studies) = 20). Stratification by regions of mainland China and Taiwan found a variation in effects across the regions, with south/southeastern (OR = 3.27; 95% CI = 1.27-8.42, N(studies) = 3) and southwestern China (OR = 2.98; 95% CI = 1.18-7.53, N(studies) = 3) experiencing the highest risk. The elevated risk associated with coal use throughout Asia was also observed when stratifying studies by gender, smoking status, sample size, design (population vs hospital case-control) and publication language. No significant publication bias was found (p(Begg's) = 0.15). CONCLUSIONS: Our results provide evidence that although the carcinogenic effect of coal use varies by location, coals from many locations exhibit elevated lung cancer risks.
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    30. 30
      Cynthia, A. A.; Edwards, R. D.; Johnson, M.; Zuk, M.; Rojas, L.; Jimenez, R. D.; Riojas-Rodriguez, H.; Masera, O. Reduction in personal exposures to particulate matter and carbon monoxide as a result of the installation of a Patsari improved cook stove in Michoacan Mexico Indoor Air 2008, 18, 93– 105
      There is no corresponding record for this reference.
    31. 31
      Van Vliet, E. D.; Asante, K.; Jack, D. W.; Kinney, P. L.; Whyatt, R. M.; Chillrud, S. N.; Abokyi, L.; Zandoh, C.; Owusu-Agyei, S. Personal exposures to fine particulate matter and black carbon in households cooking with biomass fuels in rural Ghana Environ. Res. 2013, 127, 40– 48
      There is no corresponding record for this reference.
    32. 32
      Albalak, R.; Bruce, N.; McCracken, J. P.; Smith, K. R.; De Gallardo, T. Indoor respirable particulate matter concentrations from an open fire, improved cookstove, and LPG/open fire combination in a rural Guatemalan community Environ. Sci. Technol. 2001, 35, 2650– 2655
      There is no corresponding record for this reference.
    33. 33
      Dong, R.Assessment of improvements in the Sino-Dutch rural renewable energy project. Chinese Agricultural University report to the Chinese Association of Rural Energy Industries; Chinese Agricultural University; Beijing, China, 2006.
      There is no corresponding record for this reference.

  • Supporting Information

    Supporting Information

    Table S1, Variables considered for inclusion in mixed model creation. Table S2, Linear mixed effect model indicating variables which contribute to differences between indoor and personal measurements. Table S3, Linear mixed effect modelling of ln-transformed indoor PM2.5 exposure. This material is available free of charge via the Internet at http://pubs.acs.org.


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