Integrated assessment of exposure to PM2.5 in South India and its relation with cardiovascular risk: Design of the CHAI observational cohort study

Air Pollution and Blood Pressure: Evidence From Indonesia

Indonesia faces significant air quality issues due to multiple emissions sources, including rapid urbanization and peatland fires associated with agricultural land management. Limited prior research has estimated the episodic shock of intense fires on morbidity and mortality in Indonesia but has largely ignored the impact of poor air quality throughout the year on biomarkers of cardiovascular disease risk. We conducted a cross-sectional study of the association between particulate matter less than 2.5 microns in diameter (PM2.5) and blood pressure. Blood pressure measurements were obtained from the fifth wave of the Indonesian Family Life Survey (IFLS5), an ongoing population-based socioeconomic and health survey. We used the GEOS-Chem chemical transport model to simulate daily PM2.5 concentrations at 0.5° × 0.625° resolution across the IFLS domain. We assessed the association between PM2.5 and diastolic and systolic blood pressure, using mixed effects models with random intercepts for regency/municipality and household and adjusted for individual covariates. An interquartile range increase in monthly PM2.5 exposure was associated with a 0.234 (95% CI: 0.003, 0.464) higher diastolic blood pressure, with a greater association seen in participants age 65 and over (1.16 [95% CI: 0.24, 2.08]). For the same exposure metric, there was a 1.90 (95% CI: 0.43, 3.37) higher systolic blood pressure in participants 65 and older. Our assessment of fire-specific PM2.5 yielded null results, potentially due to the timing and locations of health data collection. To our knowledge, this is the first study to provide evidence for an association between PM2.5 and blood pressure in Indonesia.

Association of ambient and household air pollution with lung function in young adults in an peri-urban area of South-India: A cross-sectional study

OBJECTIVE

Although there is evidence for the association between air pollution and decreased lung function in children, evidence for adolescents and young adults is scarce. For a peri-urban area in India, we evaluated the association of ambient PM_2.5 and household air pollution with lung function for young adults who had recently attained their expected maximum lung function.

METHODS

We measured, using a standardized protocol, forced expiratory volume in the first second (FEV₁) and forced vital capacity (FVC) in participants aged 20-26 years from the third follow-up of the population-based APCAPCS cohort (2010-2012) in 28 Indian villages. We estimated annual average PM_2.5outdoors at residence using land-use regression. Biomass cooking fuel (a proxy for levels of household air pollution) was self-reported. We fitted a within-between linear-mixed model with random intercepts by village, adjusting for potential confounders.

RESULTS

We evaluated 1,044 participants with mean age of 22.8 (SD = 1) years (range 20-26 years); 327 participants (31%) were female. Only males reported use of tobacco smoking (9% of all participants, 13% of males). The mean ambient PM_2.5 exposure was 32.9 (SD = 2.8) µg/m³; 76% reported use of biomass as cooking fuel. The adjusted association between 1 µg/m³ increase in PM_2.5 was -27 ml (95% CI, -89 to 34) for FEV₁ and -5 ml (95% CI, -93 to 76) for FVC. The adjusted association between use of biomass was -112 ml (95% CI, -211 to -13) for FEV₁ and -142 ml (95% CI, -285 to 0) for FVC. The adjusted association was of greater magnitude for those with unvented stove (-158 ml, 95% CI, -279 to -36 for FEV₁ and -211 ml, 95% CI, -386 to -36 for FVC).

CONCLUSIONS

We observed negative associations between ambient PM_2.5 and household air pollution and lung function in young adults who had recently attained their maximum lung function.

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term "smart" alongside the terms "watches", "glasses", and "jewellery". Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

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

BACKGROUND

Previous studies of the health impact of ambient and household air pollution (AAP/HAP) have chiefly relied on self-reported and/or address-based exposure modelling data. We assessed the feasibility of collecting and integrating detailed personal exposure data in different settings and seasons.

METHODS/DESIGN

We recruited 477 participants (mean age 58 years, 72% women) from three (two rural [Gansu/Henan] and one urban [Suzhou]) study areas in the China Kadoorie Biobank, based on their previously reported fuel use patterns. A time-resolved monitor (PATS+CO) was used to measure continuously for 120-hour the concentration of fine particulate matter (PM2.5) at personal and household (kitchen and living room) levels in warm (May-September 2017) and cool (November 2017-January 2018) seasons, along with questionnaires on participants' characteristics (e.g. socio-demographic, and fuel use) and time-activity (48-hour). Parallel local ambient monitoring of particulate matter (PM1, PM2.5 and PM10) and gaseous pollutants (CO, ozone, nitrogen oxides) was conducted using regularly-calibrated devices. The air pollution exposure data were compared by study sites and seasons.

FINDINGS

Overall 76% reported cooking at least weekly (regular-cooks), and 48% (urban 1%, rural 65%) used solid fuels (wood/coal) for cooking. Winter heating was more common in rural sites than in urban site (74-91% vs 17% daily), and mainly involved solid fuels. Mixed use of clean and solid fuels was common for cooking in rural areas (38%) but not for heating (0%). Overall, the measured mean PM2.5 levels were 2-3 fold higher in the cool than warm season, and in rural (e.g. kitchen: Gansuwarm_season = 142.3 µg/m3; Gansucool_season = 508.1 µg/m3; Henanwarm_season = 77.5 µg/m3; Henancool_season = 222.3 µg/m3) than urban sites (Suzhouwarm_season = 41.6 µg/m3; Suzhoucool_season = 81.6 µg/m3). The levels recorded tended to be the highest in kitchens, followed by personal, living room and outdoor. Time-resolved data show prominent peaks consistently recorded in the kitchen at typical cooking times, and sustained elevated PM2.5 levels (> 100 µg/m3) were observed in rural areas where use of solid fuels for heating was common.

DISCUSSION

Personal air pollution exposure can be readily assessed using a low-cost time-resolved monitor in different settings, which, in combination with other personal and health outcome data, will enable reliable assessment of the long-term health effects of HAP/AAP exposures in general populations.

The critical role of endothelial function in fine particulate matter-induced atherosclerosis

Ambient and indoor air pollution contributes annually to approximately seven million premature deaths. Air pollution is a complex mixture of gaseous and particulate materials. In particular, fine particulate matter (PM2.5) plays a major mortality risk factor particularly on cardiovascular diseases through mechanisms of atherosclerosis, thrombosis and inflammation. A review on the PM2.5-induced atherosclerosis is needed to better understand the involved mechanisms. In this review, we summarized epidemiology and animal studies of PM2.5-induced atherosclerosis. Vascular endothelial injury is a critical early predictor of atherosclerosis. The evidence of mechanisms of PM2.5-induced atherosclerosis supports effects on vascular function. Thus, we summarized the main mechanisms of PM2.5-triggered vascular endothelial injury, which mainly involved three aspects, including vascular endothelial permeability, vasomotor function and vascular reparative capacity. Then we reviewed the relationship between PM2.5-induced endothelial injury and atherosclerosis. PM2.5-induced endothelial injury associated with inflammation, pro-coagulation and lipid deposition. Although the evidence of PM2.5-induced atherosclerosis is undergoing continual refinement, the mechanisms of PM2.5-triggered atherosclerosis are still limited, especially indoor PM2.5. Subsequent efforts of researchers are needed to improve the understanding of PM2.5 and atherosclerosis. Preventing or avoiding PM2.5-induced endothelial damage may greatly reduce the occurrence and development of atherosclerosis.

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

Long-standing LPG subsidies, cooking fuel stacking, and personal exposure to air pollution in rural and peri-urban Ecuador

Ecuador presents a unique case study for evaluating personal air pollution exposure in a middle-income country where a clean cooking fuel has been available at low cost for several decades. We measured personal PM_2.5 exposure, stove use, and participant location during a 48-h monitoring period for 157 rural and peri-urban households in coastal and Andean Ecuador. While nearly all households owned a liquefied petroleum gas (LPG) stove and used it as their primary cooking fuel, one-quarter of households utilized firewood as a secondary fuel and 10% used induction stoves secondary to LPG. Stove use monitoring demonstrated clear within- and across-meal fuel stacking patterns. Firewood-owning participants had higher distributions of 48-h and 10-min PM_2.5 exposure as compared with primary LPG and induction stove users, and this effect became more pronounced with firewood use during monitoring.Accounting for within-subject clustering, contemporaneous firewood stove use was associated with 101 μg/m³ higher 10-min PM_2.5 exposure (95% CI: 94-108 μg/m³). LPG and induction cooking events were largely not associated with contemporaneous PM_2.5 exposure. Our results suggest that firewood use is associated with average and short-term personal air pollution exposure above the WHO interim-I guideline, even when LPG is the primary cooking fuel.

Personal exposure to particulate air pollution and vascular damage in peri-urban South India

OBJECTIVE

Air pollution is a leading preventable risk factor for cardiovascular diseases. Previous studies mostly relied on concentrations at residence, which might not represent personal exposure. Personal air pollution exposure has a greater variability compared with levels of ambient air pollution, facilitating evaluation of exposure-response functions and vascular pathophysiology. We aimed to evaluate the association between predicted annual personal exposure to PM2.5 and black carbon (BC) and three vascular damage markers in peri-urban South India.

METHODS

We analyzed the third wave of the APCAPS cohort (2010-2012), which recruited participants from 28 villages. We used predicted personal exposure to PM2.5 and BC derived from 610 participant-days of 24 h average gravimetric PM2.5 and BC measurements and predictors related to usual time-activity. Outcomes included carotid intima-media thickness (CIMT), carotid-femoral pulse wave velocity (cf-PWV) and augmentation index (AIx). We fit linear mixed models, adjusting for potential confounders and accounting for the clustered data structure. We evaluated nonlinear associations using generalized additive mixed models.

RESULTS

Of the 3017 participants (mean age 38 years), 1453 (48%) were women. The average PM2.5 exposure was 51 µg/m3 (range 13-85) for men, and 61 µg/m3 (range 40-120) for women, while the average BC was 4 µg/m3 (range 3-7) for men and 8 µg/m3 (range 3-22) for women. A 10 μg/m3 increase of PM2.5 was positively associated with CIMT (0.026 mm, 95% CI 0.014, 0.037), cf-PWV (0.069 m/s, 95% CI 0.008, 0.131) and AIx (0.8%, 95% CI 0.3, 1.3) among men. The exposure-response function for PM2.5 and AIx among men showed non-linearity, particularly within the exposure range dominated by tobacco smoking and occupational exposures. Both PM2.5 and BC were positively associated with AIx among women (0.6%, 95% CI 0.2, 1.0, per 10 μg/m3 PM2.5; 0.5%, 95% CI 0.1, 0.8, per 2 μg/m3 BC).

CONCLUSIONS

Personal exposure to particulate matter was associated with vascular damage in a peri-urban population in South India. Personal exposure to particulate matter appears to have gender-specific effects on the type of vascular damage, potentially reflecting differences in sources of personal exposure by gender.

Land-Use Change and Cardiometabolic Risk Factors in an Urbanizing Area of South India: A Population-Based Cohort Study

BACKGROUND

Land-use changes in city fringes due to urbanization can lead to a reduction of greenspace that may reduce its associated health benefits.

OBJECTIVES

We evaluated the association between changes in residential surrounding built-up land use and cardiometabolic risk factors in an urbanizing peri-urban area of south India and explored the mediating roles of air pollution, physical activity, and stress in these associations.

METHODS

We analyzed data on 6,039 adults from the third follow-up of the Andhra Pradesh Children and Parent Study (APCAPS) cohort (2010-2012). We generated trajectories of change in residential surrounding built-up land use (buffer areas) from 1995-2009 (stable, slow increase, fast increase) using remote sensing data and image classification methods. We estimated associations between built-up land use trajectories and natural log-transformed blood pressure, waist circumference, triglycerides, fasting glucose, and non-high-density lipoprotein (non-HDL) cholesterol using linear mixed models. We accounted for multiple mediators and the multilevel structure of the data in mediation analyses.

RESULTS

We observed positive associations between a fast increase in built-up land use within 300m of the home and all cardiometabolic risk factors. Compared with participants with stable trajectories, those with the largest increase in built-up land use had 1.5% (95% CI: 0.1, 2.9) higher systolic blood pressure, 2.4% (95% CI: 0.6, 4.3) higher diastolic blood pressure, 2.1% (95% CI: 0.5, 3.8) higher waist circumference, and 1.6% (95% CI: -0.6, 3.8) higher fasting glucose in fully adjusted models. Associations were positive, but not statistically significant, for triglycerides, fasting glucose, and non-HDL cholesterol. Physical activity and ambient particulate matter ≤2.5μm in aerodynamic diameter (PM2.5) partially mediated the estimated associations. Associations between fast build-up and all cardiometabolic risk factors except non-HDL cholesterol were stronger in women than men.

DISCUSSION

Increases in built-up land use surrounding residences were consistently associated with higher levels of cardiometabolic risk factors. Our findings support the need for better integration of health considerations in urban planning in rapidly urbanizing settings. https://doi.org/10.1289/EHP5445.

Identifying predictors of personal exposure to air temperature in peri-urban India

Characterizing personal exposure to air temperature is critical to understanding exposure measurement error in epidemiologic studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temperature in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and association between fixed-site ambient and personal air temperature, and identified predictors of personal air temperature based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) average personal air temperature was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) average temperature was 28.8 (2.8) °C. Agreement between average personal air and fixed-site ambient temperatures was limited, especially at night when personal air temperatures were underestimated by fixed-site temperatures (MBE = -5.6 °C). The proportion of average personal nighttime temperature variability explained by ambient fixed-site temperatures was moderate (R²_mar = 0.39); daytime associations were stronger for women (R²_mar = 0.51) than for men (R²_mar = 0.3). Other predictors of average nighttime personal air temperature included residential altitude, ceiling height, and household income. Predictors of average daytime personal air temperature included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R²_mar between ambient fixed-site and personal air temperature indicate that ambient fixed-site temperature is only a moderately useful proxy of personal air temperature in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temperatures.

Association between ambient and household air pollution with carotid intima-media thickness in peri-urban South India: CHAI-Project

BACKGROUND

Evidence linking ambient air pollution with atherosclerosis is lacking from low- and middle-income countries. Additionally, evidence regarding the association between household air pollution and atherosclerosis is limited. We evaluated the association between ambient fine particulate matter [particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5)] and biomass fuel use on carotid intima-media thickness (CIMT), a surrogate of atherosclerosis, in India.

METHODS

We analysed the third follow-up of the Andhra Pradesh Children and Parent Study cohort (2010-2012), which recruited participants from 28 peri-urban villages. Our primary outcome was mean CIMT, measured using a standardized protocol. We estimated annual average PM2.5 outdoors at residence using land-use regression. Biomass cooking fuel was self-reported. We fitted a within-between linear-mixed model adjusting for potential confounders.

RESULTS

Among 3278 participants (48% women, mean age 38 years), mean PM2.5 was 32.7 [range 24.4-38.2] µg/m3, and 60% used biomass. After confounder adjustment, we observed positive associations between within-village variation in PM2.5 and CIMT in all participants [1.79%, 95% confidence interval (CI), -0.31 to 3.90 per 1  µg/m3 of PM2.5] and in men (2.98%, 95% CI, 0.23-5.72, per 1  µg/m3 of PM2.5). Use of biomass cooking fuel was associated with CIMT in all participants (1.60%, 95% CI, -0.46 to 3.65), especially in women with an unvented stove (6.14%, 95% CI, 1.40-10.89). The point-estimate for the PM2.5 association was larger in sub-groups with higher cardiometabolic risk profile.

CONCLUSIONS

Ambient and household air pollution were positively associated with CIMT in a peri-urban population of India, although with limited precision for some estimates. We observed differences in the association between ambient and household air pollution and CIMT by gender.

Association of Ambient and Household Air Pollution With Bone Mineral Content Among Adults in Peri-urban South India

IMPORTANCE

Air pollution is a major threat to global health. Osteoporosis is responsible for a substantial burden of disease globally and is expected to increase in prevalence because of population aging. Few studies have investigated the association between air pollution and bone health, and their findings were inconclusive.

OBJECTIVE

To quantify the association between ambient and household air pollution and bone mass in a sample of the general population in peri-urban India.

DESIGN, SETTING, AND PARTICIPANTS

This was a population-based cross-sectional analysis of the Andhra Pradesh Children and Parents Study cohort, which recruited participants from 28 villages near Hyderabad, South India, during 2009 to 2012. Separate linear mixed models were fitted with nested random intercepts (household within villages) for each exposure-outcome pair and were sequentially adjusted for potential confounders. Data analysis was conducted between April 2019 and July 2019.

EXPOSURES

Annual mean ambient particulate matter air pollution less than 2.5 µm in aerodynamic diameter (PM2.5) and black carbon (BC) levels at the residence estimated by land-use regression and self-reported use of biomass cooking fuel.

MAIN OUTCOMES AND MEASURES

The primary outcome was bone mineral content (BMC) measured in grams, corrected by bone area at the lumbar spine and left hip, as measured by dual-energy x-ray absorptiometry. The secondary outcome was bone mineral density measured in grams per centimeters squared.

RESULTS

A total of 3717 participants were analyzed (mean [SD] age, 35.7 [14.0] years; 1711 [46.0%] women). The annual mean (SD) PM2.5 exposure was 32.8 (2.5) μg/m3, and the annual mean (SD) BC exposure was 2.5 (0.2) μg/m3; 57.8% of participants used biomass cooking fuels. In fully adjusted models, PM2.5 was associated with lower BMC in the spine (mean difference, -0.57 g per 3 μg/m3 increase in PM2.5; 95% CI, -1.06 to -0.07 g per 3 μg/m3 increase in PM2.5) and hip (mean difference, -0.13 g per 3 μg/m3 increase in PM2.5; 95% CI, -0.3 to 0.03 g per 3 μg/m3 increase in PM2.5). After confounder adjustment, exposure to PM2.5 was also associated with lower bone mineral density in the spine (mean difference, -0.011 g/cm2 per 3 μg/m3 increase in PM2.5; 95% CI, -0.021 to 0 g/cm2 per 3 μg/m3 increase in PM2.5) and hip (mean difference, -0.004 g/cm2 per 3 μg/m3 increase in PM2.5; 95% CI, -0.008 to 0.001 g/cm2 per 3 μg/m3 increase in PM2.5). Exposure to BC was associated with lower BMC in the spine (mean difference, -1.13 g per 1 μg/m3 increase in BC; 95% CI, -2.81 to 0.54 g per 1 μg/m3 increase in BC) and hip (mean difference, -0.35 g per 1 μg/m3 increase in BC; 95% CI, -0.96 to 0.25 g per 1 μg/m3 increase in BC), although the confidence intervals were wider. There was no association between biomass fuel use and spine BMC (mean difference, 0.12 g; 95% CI, -0.45 to 0.68 g).

CONCLUSIONS AND RELEVANCE

In a cross-sectional analysis of a population-based cohort, ambient air pollution was associated with lower BMC in a young adult population in a peri-urban area of South India.

Increased risk of carotid atherosclerosis for long-term exposure to indoor coal-burning pollution in rural area, Hebei Province, China

Smoky coal burning is a predominant manner for heating and cooking in most rural areas, China. Air pollution is associated with the risk of atherosclerosis, however, the link between indoor air pollution induced by smoky coal burning and atherosclerosis is not very clear. Therefore, we designed a cross-sectional study to evaluate the association of long-term exposure to smoky coal burning pollutants with the risk of atherosclerosis. 426 and 326 participants were recruited from Nangong, China and assigned as the coal exposure and control group according to their heating and cooking way, respectively. The indoor air quality (PM2.5, CO, SO₂) was monitored. The association between coal burning exposure and the prevalence of atherosclerosis was evaluated by unconditional logistic regression analysis, adjusted for confounding factors. The inflammatory cytokines mRNAs (IL-8, SAA1, TNF-α, CRP) expression in whole blood were examined by qPCR. People in the coal exposure group had a higher risk of carotid atherosclerosis compared with the control (risk ratio [RR], 1.434; 95% confidence interval [95%CI], 1.063 to 1.934; P = 0.018). The association was stronger in smokers, drinkers and younger (<45 years old) individuals. The elevation of IL-8 (0.24, 95%CI, 0.06-0.58; P < 0.05), CRP (0.37, 95%CI, 0.05-0.70; P < 0.05), TNF-α (0.41, 95%CI, 0.14-0.67; P < 0.01) mRNAs expression in whole blood were positively related to coal exposure. Our results suggested long-term exposure to smoky coal burning emissions could increase the risk of carotid atherosclerosis. The potential mechanism might relate that coal burning emissions exposure induced inflammatory cytokines elevation which had adverse effects on atherosclerotic plaque, and then promoted the development of atherosclerosis.

Lack of association between particulate air pollution and blood glucose levels and diabetic status in peri-urban India

BACKGROUND

Limited evidence exists on the effect of particulate air pollution on blood glucose levels. We evaluated the associations of residential and personal levels of fine particulate matter (PM_2.5) and black carbon (BC) with blood glucose and diabetic status among residents of 28 peri-urban villages in South India.

METHODS

We used cross-sectional data from 5065 adults (≥18 years, 54% men) included in the Andhra Pradesh Children and Parents Study. Fasting plasma glucose was measured once in 2010-2012 and prevalent prediabetes and diabetes were defined following the American Diabetes Association criteria. We estimated annual ambient PM_2.5 and BC levels at residence using land-use regression models and annual personal exposure to PM_2.5 and BC using prediction models based on direct measurements from a subsample of 402 participants. We used linear and logistic nested mixed-effect models to assess the association between exposure metrics and health outcomes. For personal exposures, we stratified analyses by sex.

RESULTS

Mean (SD) residential PM_2.5 and BC were 32.9 (2.6) μg/m³ and 2.5 (2.6) μg/m³, respectively; personal exposures to PM_2.5 and BC were 54.5 (11.5) μg/m³ and 5.8 (2.5) μg/m³, respectively. Average (SD) fasting blood glucose was 5.3 (1.3) mmol/l, 16% of participants had prediabetes, and 5.5% had diabetes. Residential PM_2.5 and BC were not associated with higher blood glucose levels. Personal PM_2.5 (20 μg/m³ increase) and BC (1 μg/m³ increase) were negatively associated with blood glucose levels in women (PM_2.5: -1.93, 95%CI: -3.12, -0.73; BC: -0.63, 95%CI: -0.90, -0.37). In men, associations were negative for personal PM_2.5 (-1.99, 95%CI: -3.56, -0.39) and positive for personal BC (0.49, 95%CI: -0.44, 1.43). We observed no evidence of associations between any exposure and prevalence of prediabetes/diabetes.

CONCLUSIONS

Our results do not provide evidence that residential exposures to PM_2.5 or BC are associated with blood glucose or prevalence of prediabetes/diabetes in this population. Associations with personal exposure may have been affected by unmeasured confounding, highlighting a challenge in using personal exposure estimates in air pollution epidemiology. These associations should be further examined in longitudinal studies.

Ambient Particulate Air Pollution and Blood Pressure in Peri-urban India

BACKGROUND

Evidence linking long-term exposure to particulate air pollution to blood pressure (BP) in high-income countries may not be transportable to low- and middle-income countries. We examined cross-sectional associations between ambient fine particulate matter (PM2.5) and black carbon (BC) with BP (systolic [SBP] and diastolic [DBP]) and prevalent hypertension in adults from 28 peri-urban villages near Hyderabad, India.

METHODS

We studied 5531 participants from the Andhra Pradesh Children and Parents Study (18-84 years, 54% men). We measured BP (2010-2012) in the right arm and defined hypertension as SBP ≥130 mmHg and/or DBP ≥80 mmHg. We used land-use regression models to estimate annual average PM2.5 and BC at participant's residence. We applied linear and logistic nested mixed-effect models stratified by sex and adjusted by cooking fuel type to estimate associations between within-village PM2.5 or BC and health.

RESULTS

Mean (SD) PM2.5 was 33 µg/m (2.7) and BC was 2.5 µg/m (0.23). In women, a 1 µg/m increase in PM2.5 was associated with 1.4 mmHg higher SBP (95% confidence interval [CI]: 0.12, 2.7), 0.87 mmHg higher DBP (95% CI: -0.18, 1.9), and 4% higher odds of hypertension (95% CI: 0%, 9%). In men, associations with SBP (0.52 mmHg; 95% CI: -0.82, 1.8), DBP (0.41 mmHg; 95% CI: -0.69, 1.5), and hypertension (2% higher odds; 95% CI: -2%, 6%) were weaker. No associations were observed with BC.

CONCLUSION

We observed a positive association between ambient PM2.5 and BP and hypertension in women. Longitudinal studies in this region are needed to corroborate our findings.

Implementation of clean cookstove interventions and its effects on blood pressure in low-income and middle-income countries: systematic review

OBJECTIVE

A review of the implementation outcomes of clean cookstove use, and its effects on blood pressure (BP) in low-income and middle-income countries (LMICs).

DESIGN

Systematic review of studies that reported the effect of clean cookstove use on BP among women, and implementation science outcomes in LMICs.

DATA SOURCES

We searched PubMed, Embase, INSPEC, Scielo, Cochrane Library, Global Health and Web of Science PLUS. We conducted searches in November 2017 with a repeat in May 2018. We did not restrict article publication date.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

We included only studies conducted in LMICs, published in English, regardless of publication year and studies that examined the use of improved or clean cookstove intervention on BP. Two authors independently screened journal article titles, abstracts and full-text articles to identify those that included the following search terms: high BP, hypertension and or household air pollution, LMICs, cookstove and implementation outcomes.

RESULTS

Of the 461 non-duplicate articles identified, three randomised controlled trials (RCTs) (in Nigeria, Guatemala and Ghana) and two studies of pre-post design (in Bolivia and Nicaragua) met eligibility criteria. These articles evaluated the effect of cookstove use on BP in women. Two of the three RCTs reported a mean reduction in diastolic BP of -2.8 mm Hg (-5.0, -0.6; p=0.01) for the Nigerian study; -3.0 mm Hg; (-5.7, -0.4; p=0.02) for the Guatemalan study; while the study conducted in Ghana reported a non-significant change in BP. The pre-post studies reported a significant reduction in mean systolic BP of -5.5 mm Hg; (p=0.01) for the Bolivian study, and -5.9 mm Hg (-11.3, -0.4; p=0.05) for the Nicaraguan study. Implementation science outcomes were reported in all five studies (three reported feasibility, one reported adoption and one reported feasibility and adoption of cookstove interventions).

CONCLUSION

Although this review demonstrated that there is limited evidence on the implementation of clean cookstove use in LMICs, the effects of clean cookstove on BP were significant for both systolic and diastolic BP among women. Future studies should consider standardised reporting of implementation outcomes.

The impact of air pollution on deaths, disease burden, and life expectancy across the states of India: the Global Burden of Disease Study 2017

BACKGROUND

Air pollution is a major planetary health risk, with India estimated to have some of the worst levels globally. To inform action at subnational levels in India, we estimated the exposure to air pollution and its impact on deaths, disease burden, and life expectancy in every state of India in 2017.

METHODS

We estimated exposure to air pollution, including ambient particulate matter pollution, defined as the annual average gridded concentration of PM2.5, and household air pollution, defined as percentage of households using solid cooking fuels and the corresponding exposure to PM2.5, across the states of India using accessible data from multiple sources as part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017. The states were categorised into three Socio-demographic Index (SDI) levels as calculated by GBD 2017 on the basis of lag-distributed per-capita income, mean education in people aged 15 years or older, and total fertility rate in people younger than 25 years. We estimated deaths and disability-adjusted life-years (DALYs) attributable to air pollution exposure, on the basis of exposure-response relationships from the published literature, as assessed in GBD 2017; the proportion of total global air pollution DALYs in India; and what the life expectancy would have been in each state of India if air pollution levels had been less than the minimum level causing health loss.

FINDINGS

The annual population-weighted mean exposure to ambient particulate matter PM2·5 in India was 89·9 μg/m3 (95% uncertainty interval [UI] 67·0-112·0) in 2017. Most states, and 76·8% of the population of India, were exposed to annual population-weighted mean PM2·5 greater than 40 μg/m3, which is the limit recommended by the National Ambient Air Quality Standards in India. Delhi had the highest annual population-weighted mean PM2·5 in 2017, followed by Uttar Pradesh, Bihar, and Haryana in north India, all with mean values greater than 125 μg/m3. The proportion of population using solid fuels in India was 55·5% (54·8-56·2) in 2017, which exceeded 75% in the low SDI states of Bihar, Jharkhand, and Odisha. 1·24 million (1·09-1·39) deaths in India in 2017, which were 12·5% of the total deaths, were attributable to air pollution, including 0·67 million (0·55-0·79) from ambient particulate matter pollution and 0·48 million (0·39-0·58) from household air pollution. Of these deaths attributable to air pollution, 51·4% were in people younger than 70 years. India contributed 18·1% of the global population but had 26·2% of the global air pollution DALYs in 2017. The ambient particulate matter pollution DALY rate was highest in the north Indian states of Uttar Pradesh, Haryana, Delhi, Punjab, and Rajasthan, spread across the three SDI state groups, and the household air pollution DALY rate was highest in the low SDI states of Chhattisgarh, Rajasthan, Madhya Pradesh, and Assam in north and northeast India. We estimated that if the air pollution level in India were less than the minimum causing health loss, the average life expectancy in 2017 would have been higher by 1·7 years (1·6-1·9), with this increase exceeding 2 years in the north Indian states of Rajasthan, Uttar Pradesh, and Haryana.

INTERPRETATION

India has disproportionately high mortality and disease burden due to air pollution. This burden is generally highest in the low SDI states of north India. Reducing the substantial avoidable deaths and disease burden from this major environmental risk is dependent on rapid deployment of effective multisectoral policies throughout India that are commensurate with the magnitude of air pollution in each state.

FUNDING

Bill & Melinda Gates Foundation; and Indian Council of Medical Research, Department of Health Research, Ministry of Health and Family Welfare, Government of India.

Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

Fine particulate matter (PM2.5, diameter ≤2.5 μm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 μg/m³, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7⁻35.2, regional range: 8.5⁻42.0), compared to an average of 53.7 months (95% CI: 46.3⁻61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.

When, Where, and What? Characterizing Personal PM2.5 Exposure in Periurban India by Integrating GPS, Wearable Camera, and Ambient and Personal Monitoring Data

Evidence identifying factors that influence personal exposure to air pollutants in low- and middle-income countries is scarce. Our objective was to identify the relative contribution of the time of the day ( when?), location ( where?), and individuals' activities ( what?) to PM_2.5 personal exposure in periurban South India. We conducted a panel study in which 50 participants were monitored in up to six 24-h sessions ( n = 227). We integrated data from multiple sources: continuous personal and ambient PM_2.5 concentrations; questionnaire, GPS, and wearable camera data; and modeled long-term exposure at residence. Mean 24-h personal exposure was 43.8 μg/m³ (SD 24.6) for men and 39.7 μg/m³ (SD 12.0) for women. Temporal patterns in exposure varied between women (peak exposure in the morning) and men (more exposed throughout the rest of the day). Most exposure occurred at home, 67% for men and 89% for women, which was proportional to the time spent in this location. Ambient daily PM_2.5 was an important predictor of 24-h personal exposure for both genders. Among men, activities predictive of higher hourly average exposure included presence near food preparation, in the kitchen, in the vicinity of smoking, or in industry. For women, predictors of exposure were largely related to cooking.

Development of land-use regression models for fine particles and black carbon in peri-urban South India

Land-use regression (LUR) has been used to model local spatial variability of particulate matter in cities of high-income countries. Performance of LUR models is unknown in less urbanized areas of low-/middle-income countries (LMICs) experiencing complex sources of ambient air pollution and which typically have limited land use data. To address these concerns, we developed LUR models using satellite imagery (e.g., vegetation, urbanicity) and manually-collected data from a comprehensive built-environment survey (e.g., roads, industries, non-residential places) for a peri-urban area outside Hyderabad, India. As part of the CHAI (Cardiovascular Health effects of Air pollution in Telangana, India) project, concentrations of fine particulate matter (PM_2.5) and black carbon were measured over two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2) μg/m³ for PM_2.5 and 2.7 (0.5) μg/m³ for black carbon. The LUR model for annual black carbon explained 78% of total variance and included both local-scale (energy supply places) and regional-scale (roads) predictors. Explained variance was 58% for annual PM_2.5 and the included predictors were only regional (urbanicity, vegetation). During leave-one-out cross-validation and cross-holdout validation, only the black carbon model showed consistent performance. The LUR model for black carbon explained a substantial proportion of the spatial variability that could not be captured by simpler interpolation technique (ordinary kriging). This is the first study to develop a LUR model for ambient concentrations of PM_2.5 and black carbon in a non-urban area of LMICs, supporting the applicability of the LUR approach in such settings. Our results provide insights on the added value of manually-collected built-environment data to improve the performance of LUR models in settings with limited data availability. For both pollutants, LUR models predicted substantial within-village variability, an important feature for future epidemiological studies.

Wearable camera-derived microenvironments in relation to personal exposure to PM2.5

Data regarding which microenvironments drive exposure to air pollution in low and middle income countries are scarce. Our objective was to identify sources of time-resolved personal PM_2.5 exposure in peri-urban India using wearable camera-derived microenvironmental information. We conducted a panel study with up to 6 repeated non-consecutive 24 h measurements on 45 participants (186 participant-days). Camera images were manually annotated to derive visual concepts indicative of microenvironments and activities. Men had slightly higher daily mean PM_2.5 exposure (43 μg/m³) compared to women (39 μg/m³). Cameras helped identify that men also had higher exposures when near a biomass cooking unit (mean (sd) μg/m³: 119 (383) for men vs 83 (196) for women) and presence in the kitchen (133 (311) for men vs 48 (94) for women). Visual concepts associated in regression analysis with higher 5-minute PM_2.5 for both sexes included: smoking (+93% (95% confidence interval: 63%, 129%) in men, +29% (95% CI: 2%, 63%) in women), biomass cooking unit (+57% (95% CI: 28%, 93%) in men, +69% (95% CI: 48%, 93%) in women), visible flame or smoke (+90% (95% CI: 48%, 144%) in men, +39% (95% CI: 6%, 83%) in women), and presence in the kitchen (+49% (95% CI: 27%, 75%) in men, +14% (95% CI: 7%, 20%) in women). Our results indicate wearable cameras can provide objective, high time-resolution microenvironmental data useful for identifying peak exposures and providing insights not evident using standard self-reported time-activity.

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions

This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM_2.5) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM_2.5 concentrations during June 2015-May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM_2.5 concentration was ∼30 μg m^-3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m^-3) during 2-5% of observation days. Average concentrations were ∼25 μg m^-3 higher during winter than during monsoon and ∼8 μg m^-3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM_2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM_2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations.

Development of Ahmedabad's Air Information and Response (AIR) Plan to Protect Public Health

Indian cities struggle with some of the highest ambient air pollution levels in the world. While national efforts are building momentum towards concerted action to reduce air pollution, individual cities are taking action on this challenge to protect communities from the many health problems caused by this harmful environmental exposure. In 2017, the city of Ahmedabad launched a regional air pollution monitoring and risk communication project, the Air Information and Response (AIR) Plan. The centerpiece of the plan is an air quality index developed by the Indian Institute of Tropical Meteorology’s System for Air Quality and Weather Forecasting and Research program that summarizes information from 10 new continuous air pollution monitoring stations in the region, each reporting data that can help people avoid harmful exposures and inform policy strategies to achieve cleaner air. This paper focuses on the motivation, development, and implementation of Ahmedabad’s AIR Plan. The project is discussed in terms of its collaborative roots, public health purpose in addressing the grave threat of air pollution (particularly to vulnerable groups), technical aspects in deploying air monitoring technology, and broader goals for the dissemination of an air quality index linked to specific health messages and suggested actions to reduce harmful exposures. The city of Ahmedabad is among the first cities in India where city leaders, state government, and civil society are proactively working together to address the country’s air pollution challenge with a focus on public health. The lessons learned from the development of the AIR Plan serve as a template for other cities aiming to address the heavy burden of air pollution on public health. Effective working relationships are vital since they form the foundation for long-term success and useful knowledge sharing beyond a single city.

Characterizing exposure to household air pollution within the Prospective Urban Rural Epidemiology (PURE) study

BACKGROUND

Household air pollution (HAP) from combustion of solid fuels is an important contributor to disease burden in low- and middle-income countries (LIC, and MIC). However, current HAP disease burden estimates are based on integrated exposure response curves that are not currently informed by quantitative HAP studies in LIC and MIC. While there is adequate evidence supporting causal relationships between HAP and respiratory disease, large cohort studies specifically examining relationships between quantitative measures of HAP exposure with cardiovascular disease are lacking.

OBJECTIVE

We aim to improve upon exposure proxies based on fuel type, and to reduce exposure misclassification by quantitatively measuring exposure across varying cooking fuel types and conditions in diverse geographies and socioeconomic settings. We leverage technology advancements to estimate household and personal PM2.5 (particles below 2.5 μm in aerodynamic diameter) exposure within the large (N~250,000) multi-country (N~26) Prospective Urban and Rural Epidemiological (PURE) cohort study. Here, we detail the study protocol and the innovative methodologies being used to characterize HAP exposures, and their application in epidemiologic analyses.

METHODS/DESIGN

This study characterizes HAP PM2.5 exposures for participants in rural communities in ten PURE countries with >10% solid fuel use at baseline (Bangladesh, Brazil, Chile, China, Colombia, India, Pakistan, South Africa, Tanzania, and Zimbabwe). PM2.5 monitoring includes 48-h cooking area measurements in 4500 households and simultaneous personal monitoring of male and female pairs from 20% of the selected households. Repeat measurements occur in 20% of households to assess impacts of seasonality. Monitoring began in 2017, and will continue through 2019. The Ultrasonic Personal Aerosol Sampler (UPAS), a novel, robust, and inexpensive filter based monitor that is programmable through a dedicated mobile phone application is used for sampling. Pilot study field evaluation of cooking area measurements indicated high correlation between the UPAS and reference Harvard Impactors (r = 0.91; 95% CI: 0.84, 0.95; slope = 0.95). To facilitate tracking and to minimize contamination and analytical error, the samplers utilize barcoded filters and filter cartridges that are weighed pre- and post-sampling using a fully automated weighing system. Pump flow and pressure measurements, temperature and RH, GPS coordinates and semi-quantitative continuous particle mass concentrations based on filter differential pressure are uploaded to a central server automatically whenever the mobile phone is connected to the internet, with sampled data automatically screened for quality control parameters. A short survey is administered during the 48-h monitoring period. Post-weighed filters are further analyzed to estimate black carbon concentrations through a semi-automated, rapid, cost-effective image analysis approach. The measured PM2.5 data will then be combined with PURE survey information on household characteristics and behaviours collected at baseline and during follow-up to develop quantitative HAP models for PM2.5 exposures for all rural PURE participants (~50,000) and across different cooking fuel types within the 10 index countries. Both the measured (in the subset) and the modelled exposures will be used in separate longitudinal epidemiologic analyses to assess associations with cardiopulmonary mortality, and disease incidence.

DISCUSSION

The collected data and resulting characterization of cooking area and personal PM2.5 exposures in multiple rural communities from 10 countries will better inform exposure assessment as well as future epidemiologic analyses assessing the relationships between quantitative estimates of chronic HAP exposure with adult mortality and incident cardiovascular and respiratory disease. This will provide refined and more accurate exposure estimates in global CVD related exposure-response analyses.

Performance of low-cost monitors to assess household air pollution

Exposure to household air pollution is a leading cause of morbidity and mortality globally. However, due to the lack of validated low-cost monitors with long-lasting batteries in indoor environments, most epidemiologic studies use self-reported data or short-term household air pollution assessments as proxies of long-term exposure. We evaluated the performance of three low-cost monitors measuring fine particulate matter (PM_2.5) and carbon monoxide (CO) in a wood-combustion experiment conducted in one household of Spain for 5 days (including the co-location of 2 units of HAPEX and 3 units of TZOA-R for PM_2.5 and 3 units of EL-USB-CO for CO; a total of 40 unit-days). We used Spearman correlation (ρ) and Concordance Correlation Coefficient (CCC) to assess accuracy of low-cost monitors versus equivalent research-grade devices. We also conducted a field study in India for 1 week (including HAPEX in 3 households and EL-USB-CO in 4 households; a total of 49 unit-days). Correlation and agreement at 5-min were moderate-high for one unit of HAPEX (ρ = 0.73 / CCC = 0.59), for one unit of TZOA-R (ρ = 0.89 / CCC = 0.62) and for three units of EL-USB-CO (ρ = 0.82-0.89 / CCC = 0.66-0.91) in Spain, although the failure or malfunction rate among low-cost units was high in both settings (60% of unit-days in Spain and 43% in India). Low-cost monitors tested here are not yet ready to replace more established exposure assessment methods in long-term household air pollution epidemiologic studies. More field validation is needed to assess evolving sensors and monitors with application to health studies.

Predictors of Daily Mobility of Adults in Peri-Urban South India

Daily mobility, an important aspect of environmental exposures and health behavior, has mainly been investigated in high-income countries. We aimed to identify the main dimensions of mobility and investigate their individual, contextual, and external predictors among men and women living in a peri-urban area of South India. We used 192 global positioning system (GPS)-recorded mobility tracks from 47 participants (24 women, 23 men) from the Cardiovascular Health effects of Air pollution in Telangana, India (CHAI) project (mean: 4.1 days/person). The mean age was 44 (standard deviation: 14) years. Half of the population was illiterate and 55% was in unskilled manual employment, mostly agriculture-related. Sex was the largest determinant of mobility. During daytime, time spent at home averaged 13.4 (3.7) h for women and 9.4 (4.2) h for men. Women's activity spaces were smaller and more circular than men's. A principal component analysis identified three main mobility dimensions related to the size of the activity space, the mobility in/around the residence, and mobility inside the village, explaining 86% (women) and 61% (men) of the total variability in mobility. Age, socioeconomic status, and urbanicity were associated with all three dimensions. Our results have multiple potential applications for improved assessment of environmental exposures and their effects on health.