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

Effects of Ambient Particulate Matter (PM2.5) Exposure on Calorie Intake and Appetite of Outdoor Workers

Malaysia has been experiencing smoke-haze episodes almost annually for the past few decades. PM2.5 is the main component in haze and causes harmful impacts on health due to its small aerodynamic size. This study aimed to explore the implications of PM2.5 exposure on the dietary intake of working individuals. Two phased 13-weeks follow-up study was conducted involving 440 participants, consisting of two cohorts of outdoor and indoor workers. Ambient PM2.5 concentrations were monitored using DustTrakTM DRX Aerosol Monitor. Data on Simplified Nutritional Appetite Questionnaire (SNAQ) and 24 h diet recall were collected weekly. The highest PM2.5 concentration of 122.90 ± 2.07 µg/m3 was recorded in August, and it vastly exceeded the standard value stipulated by US EPA and WHO. SNAQ scores and calorie intake were found to be significantly (p < 0.05) associated with changes in PM2.5 exposure of outdoor workers. Several moderate and positive correlations (R-value ranged from 0.4 to 0.6) were established between SNAQ scores, calorie intake and PM2.5 exposure. Overall findings suggested that long hours of PM2.5 exposure affect personal dietary intake, potentially increasing the risk of metabolic syndromes and other undesired health conditions. The current policy should be strengthened to safeguard the well-being of outdoor workers.

Bias in PM2.5 measurements using collocated reference-grade and optical instruments

Optical PM_2.5 measurements are sensitive to aerosol properties that can vary with space and time. Here, we compared PM_2.5 measurements from collocated reference-grade (beta attenuation monitors, BAMs) and optical instruments (two DustTrak II and two DustTrak DRX) over 6 months. We performed inter-model (two different models), intra-model (two units of the same model), and inter-type (two different device types: optical vs. reference-grade) comparisons under ambient conditions. Averaged over our study period, PM_2.5 measured concentrations were 46.0 and 45.5 μg m^-3 for the two DustTrak II units, 29.8 and 38.4 μg m^-3 for DRX units, and 18.3 and 19.0 μg m^-3 for BAMs. The normalized root square difference (NRMSD; compares PM_2.5 measurements from paired instruments of the same type) was ~ 5% (DustTrak II), ~ 27% (DRX), and ~ 15% (BAM). The normalized root mean square error (NRMSE; compares PM_2.5 measurements from optical instruments against a reference instrument) was ~ 165% for DustTrak II, ~ 74% after applying literature-based humidity correction and ~ 27% after applying both the humidity and BAM corrections. Although optical instruments are highly precise in their PM_2.5 measurements, they tend to be strongly biased relative to reference-grade devices. We also explored two different methods to compensate for relative humidity bias and found that the results differed by ~ 50% between the two methods. This study highlights the limitations of adopting a literature-derived calibration equation and the need for conducting local model-specific calibration. Moreover, this is one of the few studies to perform an intra-model comparison of collocated reference-grade devices.

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.

Assessment of the impact of sensor error on the representativeness of population exposure to urban air pollutants

For the monitoring of urban air pollution, smart sensors are often seen as a welcome addition to fixed-site monitoring (FSM) networks. Due to price and simple installation, increases in spatial representation are thought to be achieved by large numbers of these sensors, however, a number of sensor errors have been identified. Based on a high-resolution modelling system, up to 400 pseudo smart sensors were perturbated with the aim of simulating common sensor errors and added to the existing FSM network in Hong Kong, resulting in 1200 pseudo networks for PM_2.5 and 1040 pseudo networks for NO₂. For each pseudo network, population-weighted area representativeness (PWAR) was calculated based on similarity frequency. For PM_2.5, improvements (up to 16%) to the high baseline representativeness (PWAR = 0.74) were achievable only by the addition of high-quality sensors and favourable environmental conditions. The baseline FSM network represents NO₂ less well (PWAR = 0.52), as local emissions in the study domain resulted in high spatial pollution variation. Due to higher levels of pollution (population-weighted average 37.3 ppb) in comparison to sensor error ranges, smart sensors of a wider quality range were able to improve network representativeness (up to 42%). Marginal representativeness increases were found to exponentially decrease with existing sensor number. The quality and maintenance of added sensors had a stronger effect on overall network representativeness than the number of sensors added. Often, a small number of added sensors of a higher quality class led to larger improvements than hundreds of lower-class sensors. Whereas smart sensor performance and maintenance are important prerequisites particularly for developed cities where pollutant concentration is low and there is an existing FSM network, our study shows that for places with high pollutant variability and concentration such as encountered in some developing countries, smart sensors will provide benefits for understanding population exposure.

Characteristics and health risk assessment of fine particulate matter and surface ozone: results from Bengaluru, India

Urban air pollution is a complex problem, which requires a multi-pronged approach to understand its dynamics. In the current study, various aspects of air pollution over Bengaluru city were studied utilizing simultaneous reference-grade measurements (during the period July 2019 to June 2020) of fine particulate matter mass concentration (PM_2.5), aerosol black carbon mass concentrations (BC), and surface ozone (O₃) concentrations. The study period mean PM_2.5, BC, and O₃ were observed to be 26.8 ± 11.5 µg m^-3, 5.6 ± 2.8 µg m^-3, and 25.5 ± 12.4 ppb, respectively. Statistical methods such as principal component analysis, moving average subtraction method, conditional bivariate probability function, and concentration weighted trajectory analysis were performed to understand the dynamics of air pollution over Bengaluru and its long-range transportation pathways. Some of the major findings from the statistical analyses include (i) contrasting association in BC versus O₃ and PM_2.5 versus O₃; (ii) around one-fourth of the observed receptor site BC was contributed by local sources/emissions; and (iii) the source locations potentially contributing to BC and PM_2.5 were spatially different. In Bengaluru, long-term exposure to PM_2.5 resulted in around 3413, 3393, 1016, and 147 attributable deaths for the health endpoints chronic obstructive pulmonary disorder, ischemic heart disease, stroke, and lung cancer, respectively. Long-term exposure to O₃ resulted in around 155 attributable deaths for respiratory diseases, as estimated by the AirQ + model. Finally, the limitations of the study in terms of data availability and analysis have been detailed.

Assessing the Applicability of Photocatalytic-Concrete Blocks in Reducing the Concentration of Ambient NO2 of Chandigarh, India, Using Box–Behnken Response Surface Design Technique: A Holistic Sustainable Development Approach

Anthropogenic emissions, such as industrial, vehicular, biomass burning, and coal combustion, play a significant role in degrading the atmospheric conditions of India. Therefore, in the present study, applicability of the photocatalytic-concrete blocks was estimated in improving the ambient environment of Chandigarh, India. The photocatalytic-concrete blocks were prepared by mixing the TiO2 particles with cement. All the experiments, designed in accordance with the Box–Behnken approach, in combination with response surface methodology, were performed in a batch reactor. Further, the process parameters, namely, concentration of TiO2 (1 to 5 g), UV-A irradiance (1 to 5 mW/cm2), and relative humidity (RH) (10 to 70%), were optimized to achieve maximum degradation of NO2. Outcomes of batch experiments depicted that the maximum degradation of NO2, that is, 68.32%, was attained at 3.35 g of TiO2, 5 mW/cm2 of UV-A irradiance, and 64.60% RH. The findings of batch experiment were further theoretically applied to degrade the ambient NO2 concentration of Chandigarh, India. It was estimated that using the photocatalytic concrete for construction of Chandigarh’s pavements may reduce the ambient NO2 concentration of Chandigarh, India, to an average of 5.80 μg/m3. Afterwards, reusability of photocatalytic-concrete blocks was also assessed, and it was made evident that after five cycles, their efficiency was reduced by only 7.15%. Subsequently, it was revealed that hydrogen peroxide-based treatment of photocatalytic-concrete blocks could completely regenerate its treatment efficiency. Therefore, it is expected that the findings of this study may prove beneficial in urban planning, as it may assist scientific auditory in identifying the applicability of TiO2-based photocatalysis in mitigating the impacts of vehicular emissions.

Sensors and systems for air quality assessment monitoring and management: A review

Air quality (AQ) is a global concern for human health management. Therefore, air quality monitoring (AQM) and its management is a must-needed activity for the current world environment. A systematic review of various sensors and systems for AQ management may strengthen our understanding of the monitoring and management of AQ. Thus, the current review presents details on sensors/systems available for AQ assessment, monitoring, and management. First, we had gone through the published literature based on special keywords including AQM, Particulate Matter (PM), Carbon Mono-oxide (CO), Sulfur di-Oxide (SO2), and Nitrogen di-Oxide (NO2) among others, and identified the current scenario of research in AQ management. We discussed various sensors/systems available for the AQ management based on self-conceptualised five major categories including, ground-based AQS (wet chemistry) systems, ground-based digital sensors systems, aerial sensors systems, satellite-based sensors systems, and integrated systems. The prospects in the field of AQ assessment and management (AQA&M) were then discussed in detail. We concluded that the AQA&M can be better achieved by coupling new technologies like ground-based smart sensors, satellite remote sensing sensors, Geospatial technologies, and computational technologies like machine learning, Artificial intelligence, and Internet of Things (IoT). The current work may lead to a junction of information for connecting these sensors/systems, which is expected to be beneficial in future AQ research and management.

Fine particulate pollution concentration in Addis Ababa exceeds the WHO guideline value: Results of 3 years of continuous monitoring and health impact assessment

Real-time monitoring of fine particulate matter (PM2.5) concentrations and assessing the health impact are limited in Ethiopia. The objective of this study is to describe current levels of PM2.5 air pollution in Addis Ababa and examine temporal patterns and to consider the health impact of current PM2.5 exposure levels.

METHODS

PM2.5 concentrations were measured using a centrally-located Beta Attenuator Monitor (BAM-1022) for 3 years (1 April 2017 to 31 March 2020), with data downloaded biweekly. Deaths attributable to current PM2.5 concentration levels were estimated using the AirQ+ tool. The daily average was estimated using hourly data.

RESULTS

The daily mean (SD) PM2.5 concentration was 42.4 µg/m3 (15.98). Two daily extremes were observed: morning (high) and afternoon (low). Sundays had the lowest PM2.5 concentration, while Mondays to Thursdays saw a continuous increase; Fridays showed the highest concentration. Seasons showed marked variation, with the highest values during the wet season. Concentration spikes reflected periods of intensive fuel combustion. A total of 502 deaths (4.44%) were attributable to current air pollution levels referenced to the 35 µg/m3 WHO interim target annual level and 2,043 (17.7%) at the WHO 10 µg/m3 annual guideline.

CONCLUSION

PM2.5 daily levels were 1.7 times higher than the WHO-recommended 24-hour guideline. The current annual mean PM2.5 concentration results in a substantial burden of attributable deaths compared to an annual mean of 10 µg/m3. The high PM2.5 level and its variability across days and seasons calls for citywide interventions to promote clean air.

Impact of COVID-19 lockdown on the fine particulate matter concentration levels: Results from Bengaluru megacity, India

Leveraging the COVID-19 India-wide lockdown situation, the present study attempts to quantify the reduction in the ambient fine particulate matter concentrations during the lockdown (compared with that of the pre-lockdown period), owing to the highly reduced specific anthropogenic activities and thereby pollutant emissions. The study was conducted over Bengaluru (India), using PM_2.5 (mass concentration of particulate matter having size less than or equal to 2.5 µm) and Black Carbon mass concentration (BC) data. Open-access datasets from pollution control board (PCB) were also utilised to understand the spatial variability and region-specific reduction in PM_2.5 across the city. The highest percentage reduction was observed in BC_ff (black carbon attributable to fossil fuel combustion), followed by total BC and PM_2.5. No decrease in BC_bb (black carbon attributable to wood/biomass burning) was observed, suggesting unaltered wood-based cooking activities and biomass-burning (local/regional) throughout the study period. Results support the general understanding of multi-source (natural and anthropogenic) nature of PM_2.5 in contrast to limited-source (combustion based) nature of BC. The diurnal amplitudes in BC and BC_ff were reduced, while they remained almost the same for PM_2.5 and BC_bb. Analysis of PCB data reveal the highest reduction in PM_2.5 in an industrial cluster area. The current lockdown situation acted as a natural model to understand the role of a few major anthropogenic activities (viz., traffic, construction, industries related to non-essential goods, etc.) in enhancing the background fine particulate matter levels. Contemporary studies reporting reduction in surface fine particulate matter and satellite retrieved columnar Aerosol Optical Depth (AOD) during COVID-19 lockdown period are discussed.

Health Risk Assessment of PM2.5 and PM2.5-Bound Trace Elements in Thohoyandou, South Africa

We assessed the health risks of fine particulate matter (PM_2.5) ambient air pollution and its trace elemental components in a rural South African community. Air pollution is the largest environmental cause of disease and disproportionately affects low- and middle-income countries. PM_2.5 samples were previously collected, April 2017 to April 2018, and PM_2.5 mass determined. The filters were analyzed for chemical composition. The United States Environmental Protection Agency’s (US EPA) health risk assessment method was applied. Reference doses were calculated from the World Health Organization (WHO) guidelines, South African National Ambient Air Quality Standards (NAAQS), and US EPA reference concentrations. Despite relatively moderate levels of PM_2.5 the health risks were substantial, especially for infants and children. The average annual PM_2.5 concentration was 11 µg/m³, which is above WHO guidelines, but below South African NAAQS. Adults were exposed to health risks from PM_2.5 during May to October, whereas infants and children were exposed to risk throughout the year. Particle-bound nickel posed both non-cancer and cancer risks. We conclude that PM_2.5 poses health risks in Thohoyandou, despite levels being compliant with yearly South African NAAQS. The results indicate that air quality standards need to be tightened and PM_2.5 levels lowered in South Africa.

Measurements of Local Sources of Particulates with a Portable Monitor along the Coast of an Insular City

The air quality of modern cities is considered an important factor for the quality of life of humans and therefore is being safeguarded by various international organizations, concentrating on the mass concentration of particulate matter (PM) with an aerodynamic diameter less than 10, 2.5 and 1 μm. However, the different physical and anthropogenic processes and activities within the city contribute to the rise of fine (<1 μm) and coarse (>1 μm) particles, directly impacting human health and the environment. In order to monitor certain natural and anthropogenic events, suspecting their significant contribution to PM concentrations, seven different events taking place on the coastal front of the city of Limassol (Cyprus) were on-site monitored using a portable PM instrument; these included both natural (e.g., dust event) and anthropogenic (e.g., cement factory, meat festival, tall building construction, tire factory, traffic jam, dust road) emissions taking place in spring and summer periods. The violations of the limits that were noticed were attributed mainly to the various anthropogenic activities taking place on-site, revealing once more the need for further research and continuous monitoring of air quality.

Air pollution trend in Chandigarh city situated in Indo-Gangetic Plains: Understanding seasonality and impact of mitigation strategies

The long-term trend of air pollutants was studied in Chandigarh, located in Indo-Gangetic Plains of India. The SPM, PM₁₀, NO_x, and SO₂ depict site-specific variation having different anthropogenic activities. The results indicate that PM₁₀ levels in Chandigarh remain higher than the prescribed annual ambient air quality standards (60 μg m^-3) of India. Seasonal Kendall test indicates a declining trend of PM₁₀ for the year 2003 to 2018 at an industrial and commercial site (1996-2016). There is a significant increase in NOx levels at all locations except at commercial sites. The results of the linear regression model and Theil-Sen slope show that SPM is declining at all locations, but results are not statistically significant. Interestingly, PM₁₀ levels at the industrial site display a significant declining trend (1.3% year^-1). Similarly, NOx levels are increasing at all sites but having a statistically significant trend at a rural location (8.9% year^-1) and residential site (15.2% year^-1). Air pollutants show strong seasonal variability having a higher concentration in post-monsoon and winter season, which found to be linked with regional anthropogenic activities such as crop residue burning and use of solid biomass fuel for cooking and other purposes. Lowest PM₁₀ levels were observed during the monsoon and having a decline of 47.4-66.4% as compared to winter levels. Site-specific variations in air pollutants were found to be associated with air quality improvement policies such as shifting of an interstate bus terminal, ban on diesel autos, including strict implementation of air quality norms on industries. As the relative contribution of various pollution sources is still unknown, the seasonal pattern of pollutants will help in knowing the background concentration of pollutants and could help to formulate evidence-based policies to mitigate air pollution under National Clean Air Programme (NCAP).

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.

Spatiotemporal variations and influencing factors of PM2.5 concentrations in Beijing, China

Fine particulate matter (PM_2.5) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM_2.5 concentrations in Beijing during the 2013-2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM_2.5 concentrations based on long-term monitoring data. The results illustrated that PM_2.5 concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM_2.5 concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM_2.5 concentrations, while precipitation had essentially no effect. A high positive correlation between PM_2.5 and gaseous pollutants (SO₂, NO₂, and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM_2.5 concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this.

Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments

We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1-3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving "gold standard" measurements is needed to investigate accuracy.

A feasible experimental framework for field calibration of portable light-scattering aerosol monitors: Case of TSI DustTrak

Portable light-scattering aerosol monitors (PLSAMs) can supplement existing air quality monitoring networks through measuring air pollutant exposure concentrations at high spatiotemporal resolution. However, data collected by PLSAMs are often subject to the simplicity of measurement principle which may lead to errors compared to the regulatory data observed at fixed-site air quality monitoring stations. The main objective of this study was to develop a feasible experimental framework to assess the influence of key factors (e.g., relative humidity (RH)) on the performance of PLSAMs in the real-world conditions. Following the proposed framework, the accuracy and precision of the TSI DustTrak aerosol monitor were evaluated through side-by-side comparison with the stationary reference instruments (SRIs) while taking characteristics of particles, RH, and the concentration range into consideration. DustTrak generally demonstrated low accuracy but high precision in measuring PM_2.5 concentrations at the two selected stations. Three calibration models between DustTrak and the SRIs were used to bias correct the DustTrak PM_2.5 measurements. The RH-adjusted linear regression calibration method led to better calibration results than the simple linear regression method and the RH-adjusted empirical method, with CV R² values higher than 0.97, root mean square error less than 1.0 μg/m³, and accuracy values at 3% for two DustTraks. The proposed experimental framework can be extended to field calibration of various types of PLSAMs, and the obtained calibration results can promote a more accurate investigation of particle air pollution using these PLSAMs.

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.

Chemical fractionation, bioavailability, and health risks of heavy metals in fine particulate matter at a site in the Indo-Gangetic Plain, India

In the present study, the distribution and chemical fractionation of heavy metals (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in PM_2.5 collected at Sikandarpur in Agra from September 2015 to February 2016 were carried out to evaluate their mobility potential, environmental, and human health risk through inhalation. Sequential extraction procedure was applied to partition the heavy metals into four fractions (soluble and exchangeable fraction (F1); carbonates, oxides, and reducible fraction (F2); bound to organic matter, oxidizable, and sulphidic fraction (F3); and residual fraction (F4)) in PM_2.5 samples. The metals in each fraction were analyzed by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). Daily PM_2.5 concentration ranged between 13 and 238 μg m^-3 during the study period. For more than 92% of the days, the mass concentrations were greater than the National Ambient Air Quality Standard (NAAQS) set at 60 μg m^-3. The total mass concentration of the eight metals was 3.3 μg m^-3 that accounted for 2.5% of the PM_2.5 mass concentration and followed the order Fe > Zn > Cu > Mn > Pb > Ni > Cd > Cr in dominance. The carcinogenic metals (Cd, Cr, Ni, and Pb) comprised 10% of the total metal determined. Almost all the metals had the highest proportion in the residual fraction (F4) except Ni, which had the highest proportion in the reducible fraction (F2). Chemical fractionation and contamination factor (CF) showed that Pb and Ni are readily mobilized and more bioavailable. Risk assessment code (RAC) showed that Cd, Cu, Mn, Ni, Pb, and Zn had medium environmental risk, while Cr and Fe had low risk. When the bioavailable (F1 + F2) concentrations were applied to calculate non-carcinogenic and carcinogenic risk, the results showed that the value of hazard index (HI) for toxic metals was 1.7 for both children and adults through inhalation. The integrated carcinogenic risk was 1.8 × 10^-6 for children and 7.3 × 10^-6 for adults, with both values being higher than the precautionary criterion (1 × 10^-6). Enrichment factor (EF) calculations showed that Cd, Pb, Zn, and Ni were enriched being contributed by anthropogenic activities carried out in the industrial sectors of the city.

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.