Fine Particulate Matter Concentrations Research Articles

Seasonal Variations in Chemical Composition and Source Apportionment of Fine Particulate Matter (PM1) in an Urban Site of Jaipur City, Rajasthan

The present study was conducted with an aim to investigate the seasonal variation in mass concentration of fine particulate matter (PM1), their chemical composition, (water and soluble and non-soluble ions and other elements) and gaseous pollutants (SO2, NO2, CO and O3) at an urban site of Jaipur city in India. During summers, pollutants showed a reduction of 18.97%, 41.95%, 32.32% and 20.56% for PM1, NO2, SO2 and CO, respectively. In contrast, O3 showed an increase of 27%. The substantial reduction was also observed in the levels of secondary aerosols SO4 2- (71.15%), NO3-(21.86%), Cl- (65.63%) and K+ (8.16%). The elemental components Al, B, Be, Ca, Cu, Fe, Mn, Ni, and Pb showed a reduction in the range of 15.13 % (Al) to 71% (Cu). On the contrary, an increment was found in the levels of Ag (21.95%), Cd (62.5%), Cr (89.74%), Mg (10.43%) and Na (25.32%). Four factors were extracted by principal component analysis (PCA). In addition to the local sources, three stationary sources have been identified as contributing to the pollution load located in the WNW, NE and ENE direction of the site. It can be concluded that pollutant concentration and chemical composition of any area not only depend on the local emission, but nearby stationary sources and meteorology are significant contributors.

Biomass burning in the Neotropics is exposing migrating birds to elevated fine particulate matter concentrations

AbstractAimA unique risk faced by nocturnally migrating birds is the disorienting influence of artificial light at night (ALAN). ALAN originates from anthropogenic activities that can generate other forms of environmental pollution, including the emission of fine particulate matter (PM2.5). PM2.5 concentrations can display strong seasonal variation whose origin can be natural or anthropogenic. How this variation affects seasonal associations with ALAN and PM2.5 for nocturnally migrating bird populations has not been explored.LocationWestern Hemisphere.Time Period2021Major Taxa StudiedNocturnally migrating passerine (NMP) bird species.MethodsWe combined monthly estimates of PM2.5 and ALAN with weekly estimates of relative abundance for 164 NMP species derived using observations from eBird. We identified groups of species with similar associations with monthly PM2.5. We summarized their shared environmental, geographical, and ecological attributes.ResultsPM2.5 was lowest in North America, especially at higher latitudes during the boreal winter. PM2.5 was highest in the Amazon Basin, especially during the dry season (August–October). ALAN was highest within eastern North America, especially during the boreal winter. For NMP species, PM2.5 associations reached their lowest levels during the breeding season (<10 μg/m3) and highest levels during the nonbreeding season, especially for long‐distance migrants that winter in Central and South America (~20 μg/m3). Species that migrate through Central America in the spring encountered similarly high PM2.5 concentrations. ALAN associations reached their highest levels for species that migrate (~12 nW/cm2/sr) or spend the nonbreeding season (~15 nW/cm2/sr) in eastern North America.Main ConclusionsWe did not find evidence that the disorienting influence of ALAN enhances PM2.5 exposure during stopover in the spring and autumn for NMP species. Rather, our findings suggest biomass burning in the Neotropics is exposing NMP species to consistently elevated PM2.5 concentrations for an extended period of their annual life cycles.

Greenness and excess deaths from heat in 323 Latin American cities: Do associations vary according to climate zone or green space configuration?

Green vegetation may protect against heat-related death by improving thermal comfort. Few studies have investigated associations of green vegetation with heat-related mortality in Latin America or whether associations are modified by the spatial configuration of green vegetation. We used data from 323 Latin American cities and meta-regression models to estimate associations between city-level greenness, quantified using population-weighted normalized difference vegetation index values and modeled as three-level categorical terms, and excess deaths from heat (heat excess death fractions [heat EDFs]). Models were adjusted for city-level fine particulate matter concentration (PM2.5), social environment, and country group. In addition to estimating overall associations, we derived estimates of association stratified by green space clustering by including an interaction term between a green space clustering measure (dichotomized at the median of the distribution) and the three-level greenness variable. We stratified analyses by climate zone (arid vs. temperate and tropical combined). Among the 79 arid climate zone cities, those with moderate and high greenness levels had modestly lower heat EDFs compared to cities with the lowest greenness, although protective associations were more substantial in cities with moderate versus high greenness levels and confidence intervals (CI) crossed the null (Beta: −0.41, 95% CI: −1.06, 0.25; Beta −0.23, 95% CI: −0.95, 0.49, respectively). In 244 non-arid climate zone cities, associations were approximately null. We did not observe evidence of effect modification by green space clustering. Our results suggest that greenness may offer modest protection against heat-related mortality in arid climate zone Latin American cities.

M-quantile regression shrinkage and selection via the Lasso and Elastic Net to assess the effect of meteorology and traffic on air quality.

In this work, we intersect data on size-selected particulate matter (PM) with vehicular traffic counts and a comprehensive set of meteorological covariates to study the effect of traffic on air quality. To this end, we develop an M-quantile regression model with Lasso and Elastic Net penalizations. This allows (i) to identify the best proxy for vehicular traffic via model selection, (ii) to investigate the relationship between fine PM concentration and the covariates at different M-quantiles of the conditional response distribution, and (iii) to be robust to the presence of outliers. Heterogeneity in the data is accounted by fitting a B-spline on the effect of the day of the year. Analytic and bootstrap-based variance estimates of the regression coefficients are provided, together with a numerical evaluation of the proposed estimation procedure. Empirical results show that atmospheric stability is responsible for the most significant effect on fine PM concentration: this effect changes at different levels of the conditional response distribution and is relatively weaker on the tails. On the other hand, model selection allows to identify the best proxy for vehicular traffic whose effect remains essentially the same at different levels of the conditional responsedistribution.

Emergency department visits associated with wildfire smoke events in California, 2016–2019

Wildfire smoke has been associated with adverse respiratory outcomes, but the impacts of wildfire on other health outcomes and sensitive subpopulations are not fully understood. We examined associations between smoke events and emergency department visits (EDVs) for respiratory, cardiovascular, diabetes, and mental health outcomes in California during the wildfire season June–December 2016–2019. Daily, zip code tabulation area-level wildfire-specific fine particulate matter (PM2.5) concentrations were aggregated to air basins. A “smoke event” was defined as an air basin-day with a wildfire-specific PM2.5 concentration at or above the 98th percentile across all air basin-days (threshold = 13.5 μg/m3). We conducted a two-stage time-series analysis using quasi-Poisson regression considering lag effects and random effects meta-analysis. We also conducted analyses stratified by race/ethnicity, age, and sex to assess potential effect modification. Smoke events were associated with an increased risk of EDVs for all respiratory diseases at lag 1 [14.4%, 95% confidence interval (CI): (6.8, 22.5)], asthma at lag 0 [57.1% (44.5, 70.8)], and chronic lower respiratory disease at lag 0 [12.7% (6.2, 19.6)]. We also found positive associations with EDVs for all cardiovascular diseases at lag 10. Mixed results were observed for mental health outcomes. Stratified results revealed potential disparities by race/ethnicity. Short-term exposure to smoke events was associated with increased respiratory and schizophrenia EDVs. Cardiovascular impacts may be delayed compared to respiratory outcomes.

Impact of fine particulate matter 2.5 on hospitalization for upper respiratory tract infections in Lanzhou urban industrial area, China.

Abundant evidence has shown that an increase in the concentration of fine particulate matter 2.5 (PM2.5) leads to a simultaneous increase in the incidence of respiratory diseases. Xigu District is the main industrial district of Lanzhou, located in Lanzhou City in northwest China and central Gansu Province. Because of limited research and data in the region, the impact of PM2.5 on human health has not been systematically recognized. The aim of the study was to investigate the relationship between PM2.5 pollution and upper respiratory tract infections in urban industrial areas of Lanzhou City. Data on outpatient visits, air pollutants, and meteorological indices were collected in the Xigu District of Lanzhou City from 1 January 2013 - 31 December 2019. A generalized additive model was used to evaluate the association between PM2.5 and outpatient visits for upper respiratory tract infections. The results show that PM2.5 had the greatest impact on outpatient visits for upper respiratory tract infections on 7 cumulative lag days. At cumulative lag days 1, 3, and 5, the effects gradually increased. In the subgroup analysis, the effect of PM2.5 on visits for upper respiratory tract infections was significantly influenced by gender. Men were more susceptible to PM2.5 pollution. An increase in atmospheric PM2.5 concentration was associated with an increase in visits for upper respiratory tract infections with the lag effect. The obtained results can provide a reference for the development of prevention strategies to protect the population from the adverse effects of PM2.5 pollution.

Air pollution and suicide in rural and urban America: Evidence from wildfire smoke

Air pollution poses well-established risks to physical health, but little is known about its effects on mental health. We study the relationship between wildfire smoke exposure and suicide risk in the United States in 2007 to 2019 using data on all deaths by suicide and satellite-based measures of wildfire smoke and ambient fine particulate matter (PM2.5) concentrations. We identify the causal effects of wildfire smoke pollution on suicide by relating year-over-year fluctuations in county-level monthly smoke exposure to fluctuations in suicide rates and compare the effects across local areas and demographic groups that differ considerably in their baseline suicide risk. In rural counties, an additional day of smoke increases monthly mean PM2.5 by 0.41 μg/m3 and suicide deaths by 0.11 per million residents, such that a 1-μg/m3 (13%) increase in monthly wildfire-derived fine particulate matter leads to 0.27 additional suicide deaths per million residents (a 2.0% increase). These effects are concentrated among demographic groups with both high baseline suicide risk and high exposure to outdoor air: men, working-age adults, non-Hispanic Whites, and adults with no college education. By contrast, we find no evidence that smoke pollution increases suicide risk among any urban demographic group. This study provides large-scale evidence that air pollution elevates the risk of suicide, disproportionately so among rural populations.

Exploration of spatial heterogeneity in the effect of woodland proportion on PM2.5: An empirical study from the Yangtze River economic Belt, China

Woodland is the most important land cover type for enhancing the environmental governance capacity and ecological protection benefits. Woodland proportion is crucial in reducing the quality and concentration of atmospheric fine particulate matter (PM2.5) and improving the atmospheric environment. Existing studies have mostly examined the connection between the woodland proportion and PM2.5 on a global scale, while ignoring the geographical variability and differences in the scope and intensity of the effect. Therefore, this study investigated the regional features of the effect from the perspective of spatial heterogeneity, using a spatial agglomeration analysis method, and global and local regression analyses models with 1049 counties in the Yangtze River Economic Belt (YREB). The studies reveal that woodland proportion has a considerable impact on PM2.5, and the effect’s direction is identical to that of the expectation at the global scale. The PM2.5 concentration falls by 0.017% for every 1% increase in the woodland proportion. The influence of woodland proportion on PM2.5 showed noticeable spatial heterogeneity, and the woodland proportion in 794 counties is strongly associated with PM2.5 concentration in local areas, with 66.44% of counties having the most obvious negative effect, and the areas with the greatest effect intensity are mostly distributed in mountainous areas. The concentration of PM2.5 in the YREB exhibiting a prominent characteristics of local spatial correlation and agglomeration, and a spatial characteristics of eastward peak and an westward low. Contrastingly, the distribution of the woodland proportion were the opposite—eastward high and westward low—with noticeable spatial variability. The findings of this study offer a foundation for decision-making and theoretical support for national or regional implementation of regionally differentiated and precise measures to prevent and reduce air pollution.

Effect of neighbourhood greenness on the association between air pollution and risk of stroke first onset: A case-crossover study in shandong province, China.

Higher neighbourhood greenness is associated with beneficial health outcomes, and short-term exposure to air pollution is associated with an elevated risk of stroke onset. However, little is known about their interactions. Daily data on stroke first onset were collected from 20 counties in Shangdong Province, China, from 2013 to 2019. The enhanced vegetation index (EVI) and concentrations of fine particulate matter (PM2.5), nitrogen dioxide (NO2), ozone (O3), carbon monoxide (CO), and sulfur dioxide (SO2) were calculated for each individual at the village or community level based on their home address to measure their neighbourhood exposure to greenness and air pollution. EVI was categorised as low or high, and a time-stratified case-crossover design was used to estimate the percent excess risk (ER%) of stroke associated with short-term exposure to air pollution. We further stratified greenness on the basis of EVI values into quartiles and introduced interaction terms between air pollutant concentrations and the median EVI values of the quartiles to assess the effect of greenness on the associations between short-term exposure and stroke. Individuals living in the high-greenness areas had weaker associations between total stroke risk and exposure to NO2 (low greenness: ER%=1.765% [95% CI 1.205%-2.328%]; high greenness: ER%=0.368% [95% CI -0.252% to 0.991%]; P=0.001), O3 (low greenness: 0.476% [95% CI 0.246%-0.706%]; high greenness: ER%=0.085% [95% CI -0.156% to 0.327%]; P=0.011), and SO2 (low greenness: 0.632% [95% CI 0.138%-1.129%]; high greenness: ER%=-0.177% [95% CI -0.782% to 0.431%]; P=0.035). Residence in areas with higher greenness was related to weaker associations between air pollution and stroke risk, suggesting that effectively planning green spaces can improve public health.

Racial/ethnic disparities in PM2.5-attributable cardiovascular mortality burden in the United States.

Average ambient fine particulate matter (PM2.5) concentrations have decreased in the US in recent years, but the health benefits of this improvement among different racial/ethnic groups are unknown. We estimate the associations between long-term exposure to ambient PM2.5 and cause-specific cardiovascular disease (CVD) mortality rate and assess the PM2.5-attributable CVD deaths by race/ethnicity across 3,103 US counties during 2001-2016 (n = 595,776 county-months). A 1 µg m-3 increase in PM2.5 concentration was associated with increases of 7.16 (95% confidence interval (CI): 3.81, 10.51) CVD deaths per 1,000,000 Black people per month, significantly higher than the estimates for non-Hispanic white people (1.76 (95% CI: 1.37, 2.15); difference in coefficients: 5.40 (95% CI: 2.03, 8.77), P = 0.001). No significant difference in this association was observed between Hispanic (2.66 (95% CI: -0.03, 5.35)) and non-Hispanic white people (difference in coefficients: 0.90 (95% CI: -1.81, 3.61), P = 0.523). From 2001 to 2016, the absolute disparity in PM2.5-attributable CVD mortality burden was reduced by 44.04% between non-Hispanic Black and white people and by 2.61% between Hispanic and non-Hispanic white people. However, in 2016, the burden remained 3.47 times higher for non-Hispanic Black people and 0.45 times higher for Hispanic people than for non-Hispanic white people. We call for policies that aim to reduce both exposure and vulnerability to PM2.5 for racial/ethnic minorities.

The effects of heatwave on cognitive impairment among older adults: Exploring the combined effects of air pollution and green space

The association between heatwaves and cognitive impairment in older adults, especially the joint effect of air pollution and green space on this association, remains unknown. The present cohort study used data from waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2008 to 2018. Heatwaves were defined as having daily maximum temperature ≥ 92.5th, 95th and 97.5th percentile that continued at least two, three and four days, measured as the one-year heatwave days prior to the participants' incident cognitive impairment. Data on the annual average air pollutant concentrations of fine particulate matter (PM2.5) and ozone (O3) as well as green space exposure (according to the Normalized Difference Vegetation Index (NDVI)) were collected. Time-varying Cox proportional hazards models were constructed to examine the independent effect of heatwaves on cognitive impairment and the combined effect of heatwaves, air pollution, and green space on cognitive impairment. Potential multiplicative interactions were examined by adding a product term of air pollutants and NDVI with heatwaves in the models. The relative excess risk due to interaction (RERI) was calculated to reflect additive interactions. We found that heatwave exposure was associated with higher risks of cognitive impairment, with hazard ratios (HRs) and 95 % confidence intervals (CIs) ranging from 1.035 (95 % CI: 1.016–1.055) to 1.058 (95 % CI: 1.040–1.075). We observed a positive interaction of PM2.5 concentrations, O3 concentrations, lack of green space, and heatwave exposure on a multiplicative scale (HRs for product terms >1). Furthermore, we found a synergistic interaction of PM2.5 concentrations, O3, lack of green space, and heatwave exposure on an additive scale, with RERIs >0. These results suggest that extreme heat exposure may be a potential risk factor for cognitive impairment in older adults. Additionally, coexposure to air pollution and lack of green space exacerbated the adverse effects of heatwaves on cognitive function.

Evidence for multi-decadal fuel buildup in a large California wildfire from smoke radiocarbon measurements

In recent decades, there has been a significant increase in annual area burned in California’s Sierra Nevada mountains. This rise in fire activity has prompted the need to understand how historical forest management practices affect fuel composition and emissions. Here we examined the total carbon (TC) concentration and radiocarbon abundance (Δ14C) of particulate matter (PM) emitted by the KNP Complex Fire, which occurred during California’s 2021 wildfire season and affected several groves of giant sequoia trees in the southern Sierra Nevada. During a 26 h sampling period, we measured concentrations of fine airborne PM (PM2.5), as well as dry air mole fractions of carbon monoxide (CO) and methane (CH4), using a ground-based mobile laboratory. We also collected filter samples of PM2.5 for analysis of TC concentration and Δ14C. High correlation among PM2.5, CO, and CH4 time series confirmed that our PM2.5 measurements captured variability in wildfire emissions. Using a Keeling plot approach, we determined that the mean Δ14C of PM2.5 was 111.6 ± 7.7‰ (n = 12), which was considerably enriched relative to atmospheric carbon dioxide in the northern hemisphere in 2021 (−3.2 ± 1.4‰). Combining these Δ14C data with a steady-state one-box ecosystem model, we estimated that the mean age of fuels combusted in the KNP Complex Fire was 40 years, with a range of 29–57 years. These results provide evidence for emissions originating from woody biomass, larger-diameter fine fuels, and coarse woody debris that have accumulated over multiple decades. This is consistent with independent field observations that indicate high fire intensity contributed to widespread giant sequoia mortality. With the expanded use of prescribed fires planned over the next decade in California to mitigate wildfire impacts, our measurement approach has the potential to provide regionally-integrated estimates of the effectiveness of fuel treatment programs.

Camera traps link population-level activity patterns with wildfire smoke events for mammals in Eastern Washington State

BackgroundDue to anthropogenic climate change and historic fire suppression, wildfire frequency and severity are increasing across the western United States. Whereas the indirect effects of fire on wildlife via habitat change are well studied, less is known about the impacts of wildfire smoke on animal health and behavior. In this study, we explore the effects of wildfire smoke on the behavior of eight medium- to large-bodied mammalian species in a heterogenous study area in Washington, USA. We linked population-level activity metrics derived from camera trap data to concentrations of fire-specific fine particulate matter (PM2.5). We hypothesized that mammalian activity would decline during smoke events, as animals attempt to reduce potential health impacts of smoke inhalation. We used occupancy models and Poisson regression models to test the effect of fire-specific PM2.5 levels on daily detection probability and the number of detections per day, respectively, for each study species.ResultsWhile we did not observe any significant responses to daily mean concentrations of PM2.5 in the occupancy models, we found three species with significant responses in their rates of detections per day in the Poisson regression. Specifically, for each standard deviation increase in the daily mean concentration of PM2.5, there was a 12.9% decrease in the number of bobcat detections per day, an 11.2% decrease in the number of moose detections per day, and a 5.8% increase in the number of mule deer detections per day. In general, the effects of PM2.5 were small compared to other relevant covariates.ConclusionsWe generally found little evidence to support our hypothesis that animals would reduce their activity in response to wildfire smoke. However, our study demonstrated that mammals exhibited species-specific behavioral responses to smoke, which are possibly adaptive responses to reduce health impacts from smoke inhalation. Though we found only a few immediate behavioral responses to smoke exposure, we note that longer-term health consequences of smoke exposure for wildlife are also likely and generally unknown. Our study shows how camera traps, which are already widely used to study wildlife, can also be used to investigate the impacts of wildfire smoke on animal behavior and provides a step towards developing methods to better understand this increasing source of environmental stress on wildlife.

Using Low-Cost Sensing Technology to Assess Ambient and Indoor Fine Particulate Matter Concentrations in New York during the COVID-19 Lockdown

Air pollution is a leading cause of death in the United States and is associated with adverse health outcomes, including increased vulnerability to coronavirus disease 2019 (COVID-19). The AirBeam2 was used to measure particulate matter with a diameter of 2.5 μm or smaller (PM2.5) to investigate differences between indoor and ambient levels at seven private homes in New York during and after the COVID-19 lockdown. Measurements taken in 2020 fall, 2021 winter, and 2022 fall showed that at 90% of the sites, indoor PM2.5 levels exceeded outdoor levels both during and after the COVID-19 lockdown, p = 0.03, possibly exceeding safety levels. Higher indoor PM2.5 levels attributed to little or no ventilation in the basement and kitchens from cooking and smoke were greater in fall than in winter. Higher ambient PM2.5 levels were attributed to vehicular traffic at a street-facing sampling site. PM2.5 sources identified in this study may help in devising control strategies to improve indoor air quality (IAQ) and consequently alleviate respiratory health effects. These findings may be used as a basis for in-house modifications, including natural ventilation and the use of air purifiers to reduce exposures, mitigate future risks, and prevent potential harm to vulnerable residents.

Exploring Associations Between Short-Term Air Pollution and Daily Outpatient Visits for Allergic Rhinitis.

Many studies have reported that exposure to air pollution increases the likelihood of acquiring allergic rhinitis (AR). This study investigated associations between short-term air pollution exposure and AR outpatient visits. The Department of Otorhinolaryngology, Affiliated Hospital of Hangzhou Normal University provided AR outpatient data from January 1, 2019 to December 31, 2021. Daily air quality information for that period was gathered from the Hangzhou Air Quality Inspection Station. We used the Poisson's generalized additive model (GAM) to investigate relationships between daily outpatient AR visits and air pollution, and investigated lag-exposure relationships across days. Subgroup analyses were performed by age (adult (>18 years) and non-adult (<18 years)) and sex (male and female). We recorded 20,653 instances of AR during the study period. Each 10 g/m3 increase in fine particulate matter (PM10 and PM2.5) and carbon monoxide (CO) concentrations was associated with significant increases in AR outpatient Visits. The relative risks (RR) were: 1.007 (95% confidence interval (CI): 1.001-1.013), 1.026 (95% CI: 1.008-1.413), and 1.019 (95% CI: 1.008-1.047). AR visits were more likely due to elevated PM2.5, PM10, and CO levels. Additionally, children were more affected than adults. To better understand the possible effects of air pollution on AR, short-term exposure to ambient air pollution (PM2.5, PM10, and CO) may be linked to increased daily outpatient AR visits.

Evaluation of the Chemical Characteristics and Predictive Model of Water-Soluble Inorganic Ions for Fine Particulate Matter Generated in Pohang

Objectives:This study aims to contribute to establishing the regional effective management of fine particulate matter by evaluating the chemical characteristics and contribution of fine particulate matter, and the accuracy of predictive model of fine particulate matter through the measurement of water-soluble inorganic ions (WSIIs) and electrical conductivity for fine particulate matter generated in Pohang.Methods:PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; samples were simultaneously collected using a low volume air sampler from April to November 2022. For sample analysis, cations of Ca&lt;sup&gt;2+&lt;/sup&gt;, Mg&lt;sup&gt;2+&lt;/sup&gt;, K&lt;sup&gt;+&lt;/sup&gt;, NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, Na&lt;sup&gt;+&lt;/sup&gt; and anions of Cl&lt;sup&gt;-&lt;/sup&gt;, NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt;, SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2-&lt;/sup&gt;, and electrical conductivity were measured after pretreatment by ultrasonic extraction. Results and Discussion:The average concentrations of WSIIs for PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt; in Pohang were 12.1μg/m&lt;sup&gt;3&lt;/sup&gt; and 8.5μg/m&lt;sup&gt;3&lt;/sup&gt;, respectively, accounting for 35.5% and 50.0% of each fine particulate matter. The sum of NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt;, SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2-&lt;/sup&gt; concentration was found to account for the majority of 71% and 78% of WSIIs in PM&lt;sub&gt;10&lt;/sub&gt; and PM&lt;sub&gt;2.5&lt;/sub&gt;, respectively. The PM&lt;sub&gt;2.5&lt;/sub&gt;/PM&lt;sub&gt;10&lt;/sub&gt; ratios for NH&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;+&lt;/sup&gt;, K&lt;sup&gt;+&lt;/sup&gt;, and SO&lt;sub&gt;4&lt;/sub&gt;&lt;sup&gt;2-&lt;/sup&gt; were 95%, 89%, and 81%, respectively, mostly present in PM&lt;sub&gt;2.5&lt;/sub&gt;. The average ratio of PM&lt;sub&gt;2.5&lt;/sub&gt;/PM&lt;sub&gt;10&lt;/sub&gt; for NO&lt;sub&gt;3&lt;/sub&gt;&lt;sup&gt;-&lt;/sup&gt; was 54%, but it rose sharply to 79% in November when the temperature was low, indicating an increase in contribution to the generation of PM&lt;sub&gt;2.5&lt;/sub&gt; in winter. During the sampling period excluding April and July, the ion balance for cations and anions was relatively good at a 1:1 ratio and showed chemical properties of fine particulate matter close to neutral. A regression model was evaluated for the measured electrical conductivity of WSIIs and the concentration of fine particulate matter. The MAE and RMSE values for PM&lt;sub&gt;2.5&lt;/sub&gt; were 1.8μg/m&lt;sup&gt;3&lt;/sup&gt; and 2.4μg/m&lt;sup&gt;3&lt;/sup&gt;, respectively, which were lower than PM&lt;sub&gt;10&lt;/sub&gt; (MAE 7.5 μg/m&lt;sup&gt;3&lt;/sup&gt;, RMSE 10.3μg/m&lt;sup&gt;3&lt;/sup&gt;), indicating high precision and accuracy. Conclusion:This study confirmed the origin of fine particulate matter generated in Pohang through WSIIs analysis, and suggested that the measured electrical conductivity of WSIIs could be used as a key parameter for measuring the concentration of fine particulate matter.

A Simulation Study on the Influence of Street Tree Configuration on Fine Particulate Matter (PM2.5) Concentration in Street Canyons

Because motor vehicles emit a large amount of PM2.5 pollution, traffic-related emissions have always been an important part of PM2.5 pollution. To better understand the influence of street trees on traffic-related PM2.5 pollution, our study focused on camphor trees, common evergreen urban street trees in central and southern China. We used ENVI-met for the simulation of PM2.5 pollution and to build a model to show the distribution of PM2.5 pollution along a section of Xinyao North Road in downtown Changsha City in central China. Based on this model, we constructed four other models with different heights, quantities, and distances between street trees, where each model had high feasibility and aimed to determine how these affect the PM2.5 concentration on the designated block. We performed simulations within different time frames in the year. We found that the wind can promote the diffusion of PM2.5 in the street canyon. Too dense a distribution of tall street trees will have a negative impact on PM2.5 concentration in street canyons. A moderate distance between street trees is conducive to the dispersion of pollutants. Because the crown of 5 m high street trees is small, its negative impact on the dispersion of wind and PM2.5 is relatively small, so further increasing the number of 5 m high street trees in street canyons with densely distributed tall street trees will have only a little more negative impact on PM2.5 concentration in street canyons. The PM2.5 concentration in the street canyon is generally better when the street trees are 5 m long, even if the number of 5 m high street trees is relatively large. Although the crown size of 15 m high street trees is larger than that of 10 m street trees, the vertical distance between the canopy of 15 m high street trees and the ground is usually greater than that of 10 m high street trees. The distance between the canopy of 15 m high street trees and the breathing zone is usually greater than that of the 10 m street trees. Longer distances lead to a weakening of its impact on PM2.5. When the 15 m high and 10 m high street trees are more scattered in the street, their effects on the PM2.5 concentration at the height of the breathing zone (1.5 m) are generally similar.

The infiltration of wildfire smoke and its potential dose on pregnant women: Lessons learned from Indonesia wildfires in 2019

The occurrence of wildfires in Indonesia is prevalent during drought seasons. Multiple toxic pollutants emitted from wildfires have deleterious effects on pregnant women. However, the evidence for these on pregnant women was underreported. The study conducted 24-h monitoring of fine particulate matter (PM2.5) concentrations indoors and outdoors in 9 low-income homes in Palangka Raya during the 2019 wildfire season and 6 low-income homes during the 2019 non-wildfire season. A hundred and seventy pregnant women had their PM exposure assessed between July and October 2019 using personal monitors. It was observed that outdoor air pollutant levels were greater than those found indoors without indoor sources. The findings indicate that indoor PM2.5 concentrations were modestly increased by 1.2 times higher than outdoor, suggesting that buildings only partially protected people from exposure during wildfires. The concentrations of PM2.5 were found to be comparatively higher indoors in residential buildings with wood material than in brick houses. The study findings indicate that 8 out of 12 brick houses exhibited a notable RI/O24 h of less than 1 during the wildfires, whereas all I/O24 h ratios during the non-wildfire season were >1, suggesting the influence of indoor sources. Based on the estimation of daily PM2.5 dose, pregnant women received around 21% of their total daily dose during sedentary activity involving cooking. The present research offers empirical support for the view that indoor air quality in low-income households is affected by a complex combination of factors, including wildfire smoke, air tightness, and occupant behaviour. Also, this situation is more likely a potential risk to pregnant women being exposed to wildfire smoke.

Association between spill-related exposure to fine particulate matter and peripheral motor and sensory nerve function among oil spill response and cleanup workers following the Deepwater Horizon oil spill.

Burning/flaring of oil/gas during the Deepwater Horizon oil spill response and cleanup (OSRC) generated high concentrations of fine particulate matter (PM2.5). Personnel working on the water during these activities may have inhaled combustion products. Neurologic effects of PM2.5 have been reported previously but few studies have examined lasting effects following disaster exposures. The association of brief, high exposures and adverse effects on sensory and motor nerve function in the years following exposure have not been examined for OSRC workers. We assessed the relationship between exposure to burning/flaring-related PM2.5 and measures of sensory and motor nerve function among OSRC workers. PM2.5 concentrations were estimated from Gaussian plume dispersion models and linked to self-reported work histories. Quantitative measures of sensory and motor nerve function were obtained 4-6 years after the disaster during a clinical exam restricted to those living close to two clinics in Mobile, AL or New Orleans, LA (n = 3401). We obtained covariate data from a baseline enrollment survey and a home visit, both in 2011-2013. The analytic sample included 1186 participants. We did not find strong evidence of associations between exposure to PM2.5 and sensory or motor nerve function, although there was a suggestion of impairment based on single leg stance among individuals with high exposure to PM2.5. Results were generally consistent whether we examined average or cumulative maximum exposures or removed individuals with the highest crude oil exposures to account for co-pollutant confounding. There was no evidence of exposure-response trends. Remediating environmental disasters is essential for long-term human and environmental health. During the Deepwater Horizon oil spill disaster, burning and flaring of oil and gas were used to remove these pollutants from the environment, but led to potentially high fine particulate matter exposures for spill response workers working on the water. We investigate the potential adverse effects of these exposures on peripheral nerve function; understanding the potential health harm of remediation tactics is necessary to inform future clean up approaches and protect human health.

Understanding the time-activity pattern to improve the measurement of personal exposure: An exploratory and experimental research

Although ambient fine particulate matter (PM2.5) concentrations and their components are commonly used as proxies for personal exposure monitoring, developing an accurate and cost-effective method to use these proxies for personal exposure measurement continues to pose a significant challenge. Herein, we propose a scenario-based exposure model to precisely estimate personal exposure level of heavy metal(loid)s (HMs) using scenario HMs concentrations and time-activity patterns. Personal exposure levels and ambient pollution levels for PM2.5 and HMs differed significantly with corresponding personal/ambient ratios of approximately 2, and exposure scenarios could narrow the assessment error gap by 26.1–45.4%. Using a scenario-based exposure model, we assessed the associated health risks of a large sample population and identified that the carcinogenic risk of As exceeded 1 × 10−6, while we observed non-carcinogenic risks from As, Cd, Ni, and Mn in personal exposure to PM2.5. We conclude that the scenario-based exposure model is a preferential alternative for monitoring personal exposure compared to ambient concentrations. This method ensures the feasibility of personal exposure monitoring and health risk assessments in large-scale studies.