Daily Average PM2 Research Articles

Appraisal of regional haze event and its relationship with PM2.5 concentration, crop residue burning and meteorology in Chandigarh, India

Air pollution affects not only the air quality in megacities but also in medium and small-sized cities due to rapid urbanization, industrialization, and other anthropogenic activities. From October 28, 2015 to November 3, 2015, the Indo-Gangetic Plains region, including Chandigarh encountered an episode of poor visibility during the daytime. The daily average PM2.5 concentration reached 191 μg/m3, and visibility reduced by ∼2.2 times in the Chandigarh region. PM2.5 concentration was found around 4 times higher than a non-haze day and more than 3 times higher than National Ambient Air Quality Standards for 24 h. A significant correlation between PM2.5 and CO (r: 0.87) during the haze period indicated similarity in their emission sources; which was attributed to the burning of solid organic matter. Further, satellite data and back-trajectory analysis of air masses showed large-scale rice stubble burning in the agricultural fields, adjoining to the city areas. The transboundary movement of air masses below 500 m and meteorological conditions played a major role in building the pollution load in the Chandigarh region. Moreover, the enhanced concentration of biomass burning tracers, i.e., organic carbon (∼3.8 times) and K+ ions (2∼ times) in PM2.5 and acetonitrile (∼2.3 times) in ambient air was observed during the haze event. The study demonstrates how regional emissions and meteorological conditions can affect the air quality in a city; which can be useful for proper planning and mitigation policies to minimize high air pollution episodes.

Solar PV in Kuwait: The effect of ambient temperature and sandstorms on output variability and uncertainty

Kuwait has a high potential for utilizing meteorologically driven energy resources such as solar PV. However, understanding the extent to which the distinct climatic conditions in Kuwait, reflected in the ambient temperature and occurrence of sandstorms, affect the variability and uncertainty of solar PV output is crucial. This is because it allows power system planners to adopt appropriate measures to accommodate solar PV generation and thus minimize renewable energy curtailment and consequently opportunity costs. In this study, the performance of a 2000 MW solar PV plant operating under the weather conditions in Kuwait is simulated using a Monte Carlo approach. The results show, on average, the power generation is 13% lower in the summer compared with the spring when the temperature is milder and solar production peaks. However, the certainty of production is higher in the summer. Furthermore, the PV generation variability in the summer is of the same magnitude as the demand variability, whereas during winter months, it is approximately twice the demand variability on any given day of the year. Sandstorms with daily average PM10 concentration of less than 300 ppb lead to little to no reduction in daily total irradiation. However, day-long and severe sandstorms (PM10 2700 ppb or greater) reduce daily total irradiation by 57%. Generally, the largest reductions occur when high particulate matter concentrations coincide with peak hourly solar irradiance.

Characteristics and source apportionment of water-soluble inorganic ions in PM2.5 during a wintertime haze event in Huanggang, central China

Winter haze events are frequent occurrences in central China, however, their characteristics and formational mechanisms in the region are still poorly understood. In this study, we conducted continuous observations of PM2.5 during a haze event (from January 13 to 24, 2018) in Huanggang in central China to identify the characteristics of water-soluble inorganic ions (WSIIs) associated with PM2.5. The observation period was divided into the pre-haze episode (Pre-HE, January 13 to 14), haze episode (HE, January 15 to 21) and post-haze episode (Post-HE, January 22 to 24). The daily average PM2.5 mass concentration during the Pre-HE, HE and Post-HE was 51.7, 134.9 and 92.2 μg m−3, respectively. The WSIIs accounted for 57.3% of the total PM2.5 content throughout the entire observation period and were predominantly composed of nitrate, sulfate, and ammonium. During the HE, the major ions predominantly occurred in the form of (NH4)2SO4, NH4NO3, NH4Cl, and HCl. Moreover, the sulfur oxidation ratios and nitrogen oxidation ratios were relatively high, inferring the high contribution of secondary transformation. During the HE, homogeneous and heterogeneous reactions played an important role in the formation of nitrate and sulfate in PM2.5, respectively. The results of the principal component analysis-multiple linear regression suggest that the major sources of WSIIs were secondary inorganic aerosols, combustion and dust. The results of the backward trajectory analysis and the potential source contribution function model infer the strong influence of both local and regional air masses on haze formation and dissipation in Huanggang.

Comparison Analysis of the Effect of Emission Reduction Measures for Major Events and Heavy Air Pollution in the Capital

Taking the "9.3" military parade in 2015 and two red alerts of heavy air pollution in December of the same year as examples, the characteristics of meteorological factors and pollutant concentration variation were compared. Based on the estimation of pollutant emission reduction under different periods, the WRF-CAMx model was used to evaluate the effect of PM2.5 pollution improvement. The results showed that the daily average PM2.5 concentration (19.0 μg·m-3) during the parade (from August 20 to September 4) decreased by 60.0% and 48.0%, respectively, in comparison to that before (August 15-19) and after (September 5-15) the parade. The daily average PM2.5 concentration (232.3 μg·m-3) during the first red alert period was higher than that of the second red alert (216.6 μg·m-3). The air quality before the second red alert was better than that before the first red alert. The proportion of emission reduction during the parade was generally larger than that during the red alert periods, which provided a controllable and favorable condition for the realization of the "Parade Blue". The PM2.5 concentration in Beijing decreased by 32.4%, 17.1%, and 22.0% under the control measures during the periods of the "9.3" parade, the first red alert, and second red alert, respectively. The higher proportion of PM2.5 concentration reduction could be attributed to the more intensive regional emission reduction and the favorable meteorological conditions. The intensity of the pollution reduction, the timing of the implementation of emergency control measures, and meteorological conditions were the most important factors that may have influenced the improvement of pollution.

Development of Hourly Indoor PM2.5 Concentration Prediction Model: The Role of Outdoor Air, Ventilation, Building Characteristic, and Human Activity.

Exposure to indoor particulate matter less than 2.5 µm in diameter (PM2.5) is a critical health risk factor. Therefore, measuring indoor PM2.5 concentrations is important for assessing their health risks and further investigating the sources and influential factors. However, installing monitoring instruments to collect indoor PM2.5 data is difficult and expensive. Therefore, several indoor PM2.5 concentration prediction models have been developed. However, these prediction models only assess the daily average PM2.5 concentrations in cold or temperate regions. The factors that influence PM2.5 concentration differ according to climatic conditions. In this study, we developed a prediction model for hourly indoor PM2.5 concentrations in Taiwan (tropical and subtropical region) by using a multiple linear regression model and investigated the impact factor. The sample comprised 93 study cases (1979 measurements) and 25 potential predictor variables. Cross-validation was performed to assess performance. The prediction model explained 74% of the variation, and outdoor PM2.5 concentrations, the difference between indoor and outdoor CO2 levels, building type, building floor level, bed sheet cleaning, bed sheet replacement, and mosquito coil burning were included in the prediction model. Cross-validation explained 75% of variation on average. The results also confirm that the prediction model can be used to estimate indoor PM2.5 concentrations across seasons and areas. In summary, we developed a prediction model of hourly indoor PM2.5 concentrations and suggested that outdoor PM2.5 concentrations, ventilation, building characteristics, and human activities should be considered. Moreover, it is important to consider outdoor air quality while occupants open or close windows or doors for regulating ventilation rate and human activities changing also can reduce indoor PM2.5 concentrations.

Fine particulate matter constituents and heart rate variability: A panel study in Shanghai, China

BackgroundShort-term exposure to fine particulate matter (PM2.5) has been associated with reduced heart rate variability (HRV), an established indicator of cardiac autonomic function, but it remains uncertain which specific constituents of PM2.5 had key impacts. ObjectiveTo examine the short-term associations between various PM2.5 constituents and HRV measures. MethodsWe conducted a retrospective panel study among 78 participants who received repeated 24-h electrocardiogram testing in Shanghai, China from 2015 to 2019. We obtained daily concentrations of 14 main chemical constituents of PM2.5 from a fixed-site monitor. During 3 or 4 rounds of follow-ups, we measured 6 HRV parameters, including 3 frequency-domain parameters (power in very low frequency, low frequency and high frequency) and 3 time-domain parameters (standard deviation of normal-to-normal intervals, root mean square successive difference and percent of adjacent normal R-R intervals with a difference ≥50 msec). We used linear mixed-effects models to analyze the data after controlling for time trends, environmental and individual risk factors. ResultsThe average daily PM2.5 exposure was 45.8 μg/m3 during the study period. The present-day exposure to PM2.5 had the strongest negative influences on various HRV indicators. These associations attenuated greatly on lag 1 d or lag 2 d. Elemental carbon, organic carbon, nitrate, sulfate, arsenic, cadmium, chromium and nickel were consistently associated with reduced HRV parameters in both single-constituent models and constituent-PM2.5 models. ConclusionOur study highlighted the key roles of traffic-related components of PM2.5 in inhibiting cardiac autonomic function.

Influence of local meteorology on the chemical characteristics of fine particulates in Metropolitan Manila in the Philippines

The chemical characteristics of fine particulates in Metropolitan Manila, capital of the Philippines, were investigated. Week-long daily PM2.5 sampling observations were done in the months of May and October of 2018 in the Manila Bay area. May and October correspond to the boreal spring and fall monsoon transition periods, respectively. The sampling periods were selected to minimize the contribution of transboundary pollutants and to determine the influence of local mesoscale weather under similar synoptic flow conditions. Results show the daily average PM2.5 mass concentration for spring and fall to be 29.07 ±4.69 and 50.06 ±8.79 μg m−3, respectively. Most PM2.5 components were also found to be higher during fall. The difference in concentration between the two seasons is mainly due to the strong land/sea-breeze in spring induced by warmer land surface temperature. In spring, strong sea-breeze disperses PM2.5 inland, and the nighttime land-breeze serves as the outflow path of fine aerosols from Metropolitan Manila. In fall, the opposing flow of the weaker sea-breeze to the prevailing easterly produces stagnant air that leads to higher accumulated particulate concentration. Moreover, the suppressed outflow during fall season resulted in higher PM2.5 contribution of aged secondary aerosol components. Overall, the largest contributors to Metropolitan Manila PM2.5 mass concentration are the primary and secondary aerosols from local vehicular emissions, followed by soil dust and seasalt.

Air quality changes during the COVID-19 lockdown over the Yangtze River Delta Region: An insight into the impact of human activity pattern changes on air pollution variation

The outbreak of COVID-19 has spreaded rapidly across the world. To control the rapid dispersion of the virus, China has imposed national lockdown policies to practise social distancing. This has led to reduced human activities and hence primary air pollutant emissions, which caused improvement of air quality as a side-product. To investigate the air quality changes during the COVID-19 lockdown over the YRD Region, we apply the WRF-CAMx modelling system together with monitoring data to investigate the impact of human activity pattern changes on air quality. Results show that human activities were lowered significantly during the period: industrial operations, VKT, constructions in operation, etc. were significantly reduced, leading to lowered SO2, NOx, PM2.5 and VOCs emissions by approximately 16–26%, 29–47%, 27–46% and 37–57% during the Level I and Level II response periods respectively. These emission reduction has played a significant role in the improvement of air quality. Concentrations of PM2.5, NO2 and SO2 decreased by 31.8%, 45.1% and 20.4% during the Level I period; and 33.2%, 27.2% and 7.6% during the Level II period compared with 2019. However, ozone did not show any reduction and increased greatly. Our results also show that even during the lockdown, with primary emissions reduction of 15%–61%, the daily average PM2.5 concentrations range between 15 and 79 μg m−3, which shows that background and residual pollutions are still high. Source apportionment results indicate that the residual pollution of PM2.5 comes from industry (32.2–61.1%), mobile (3.9–8.1%), dust (2.6–7.7%), residential sources (2.1–28.5%) in YRD and 14.0–28.6% contribution from long-range transport coming from northern China. This indicates that in spite of the extreme reductions in primary emissions, it cannot fully tackle the current air pollution. Re-organisation of the energy and industrial strategy together with trans-regional joint-control for a full long-term air pollution plan need to be further taken into account.

Influence of zero-burning policy and climate phenomena on ambient PM2.5 patterns and PAHs inhalation cancer risk during episodes of smoke haze in Northern Thailand

Abstract Air pollution from open burning in the Southeast Asian region has become an important environmental issue in the past decade, mainly due to its impacts on the economy and human health. Daily samples (24-h sampling) of particulate matter having an aerodynamic diameter equal to or less than 2.5 μm (PM2.5) were collected during the dry season (March–April) in the years 2017 and 2018 from two sampling sites: one in a suburban area of Chiang Mai Province (CM) that is regularly impacted by forest fires during the dry season, and one near an agricultural residue burning source located in Nan Province (NN). Daily average PM2.5 concentrations measured in Chiang Mai from both years (around 38–42 μg/m3) were about 2 times lower when compared to the same time period prior to the implementation of the zero-burning policy (2012–2015) with an average PM2.5 of 67.1 ± 34.1 μg/m3. Comparisons made during La Nina years (which are usually less affected by open burning), i.e. in 2012 (56 μg/m3), make it clear that a drop in PM2.5 values was influenced by the implementation of the zero-burning policy that was enforced for a period of about 60 days in an attempt to control open burning practices in upper Northern Thailand. Based on the patterns of PM2.5 concentrations observed during the dry season, it was determined that the policy could reduce open burning activities during the periods of the policy implementation. However, the zero-burning policy has also contributed to prolonging the smoke haze situation from a 2-month period (mid Feb –mid Apr) to a 3-month-long period (mid Feb–mid May). Moreover, health risks from the inhalation of PM2.5 bound PAHs is of great concern. PM2.5 concentrations at the two study sites were not significantly different, but average concentrations of 16 PAHs at the NN station for both years ( 5 ng/m3) were higher than those at the CM station ( 2 ng/m3). Therefore, exposure to PM2.5 at Nan area was more hazardous than at Chiang Mai. The ratio of some PAHs, i.e. BaA/(BaA + CHR) and IND/(IND + BPER), were used to determine the potential pollutant sources during the dry season, and it was found that PM2.5 at both sampling sites probably originated from similar source origins, i.e. wide-spread biomass burning (major source) and traffic emissions (minor source). In addition, not only are fine particulate matters a concern, but their chemical composition, particularly the levels of carcinogenic compounds that are bound to the PMs, are also believed to be a significant cause of these adverse health effects.

Saharan sand and dust storms and neonatal mortality: Evidence from Burkina Faso

West African populations are exposed to the longest and harshest dust storms on the planet, the Saharan sand and dust storms (SDS). Nonetheless, little is known about the effects of the severe storms on early-life health in West Africa. This study investigated the association of the risk of neonatal mortality, an indicator of the population's early-life health, with potential prenatal and neonatal exposure to the Saharan SDS. Data on 30,552 under-five children from Burkina Faso's 1993, 2003, and 2010 demographic and health surveys were matched to the particulate matters (PM) and terrestrial air temperature and precipitation forecasts. Exposure to dust events was measured by the number of days with average PM10 and PM2.5 concentrations above a series of threshold. Intensity-dependent patterns of associations between neonatal mortality and both prenatal and birth month exposure to dust events were identified. There was no association if average daily PM10 and PM2.5 levels were <60 and 30 μg/m3, respectively. However, strong associations, which increase almost linearly with the intensity of exposure, were identified when daily PM10 and PM2.5 levels ranged from 70 to 150 and from 40 to 70 μg/m3, respectively. At the higher PM levels, the association for the gestation period decreased, but that for the birth month remained mostly unresponsive to changes in the PM levels. Larger associations were identified when siblings were compared.

Substantial Increase in the Joint Occurrence and Human Exposure of Heatwave and High‐PM Hazards Over South Asia in the Mid‐21st Century

AbstractExtreme heat occurrence worldwide has increased in the past decades. Greenhouse gas emissions, if not abated aggressively, will lead to large increases in frequency and intensity of heat extremes. At the same time, many cities are facing severe air pollution problems featuring high‐PM episodes that last from days to weeks. Based on a high‐resolution decadal‐long model simulation using a state‐of‐the‐science regional chemistry‐climate model that is bias corrected against reanalysis, here we show that when daily average wet‐bulb temperature of 25 °C is taken as the threshold for severe health impacts, heat extremes frequency averaged over South Asia increases from 45 ± 5 days/year in 1997–2004 to 78 ± 3 days/year in 2046–2054 under RCP8.5 scenario. With daily averaged PM2.5 surface concentration of 60 μg/m3 defined as the threshold for such “unhealthy” extremes, high‐PM extremes would occur 132 ± 8 days/year in the Decade 2050 under RCP8.5. Even more concerning, due to the potential health impacts of two stressors acting in tandem, is the joint occurrence of the heatwave and high‐PM hazard (HHH), which would have substantial increases of 175% in frequency and 79% in duration. This is in contrast to the 73–76% increase for heatwave or high PM when assessed individually. The fraction of land exposed to prolonged HHH increases by more than tenfold in 2050. The alarming increases in just a few decades pose great challenges to adaptation and call for more aggressive mitigation. For example, under a lower emission pathway, the frequency of HHH will only increase by 58% with a lower frequency of high‐PM extremes.

The delayed effect of wildfire season particulate matter on subsequent influenza season in a mountain west region of the USA

Particularly in rural settings, there has been little research regarding the health impacts of fine particulate matter (PM2.5) during the wildfire season smoke exposure period on respiratory diseases, such as influenza, and their associated outbreaks months later. We examined the delayed effects of PM2.5 concentrations for the short-lag (1–4 weeks prior) and the long-lag (during the prior wildfire season months) on the following winter influenza season in Montana, a mountainous state in the western United States. We created gridded maps of surface PM2.5 for the state of Montana from 2009 to 2018 using spatial regression models fit with station observations and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical thickness data. We used a seasonal quasi-Poisson model with generalized estimating equations to estimate weekly, county-specific, influenza counts for Montana, associated with delayed PM2.5 concentration periods (short-lag and long-lag effects), adjusted for temperature and seasonal trend. We did not detect an acute, short-lag PM2.5 effect nor short-lag temperature effect on influenza in Montana. Higher daily average PM2.5 concentrations during the wildfire season was positively associated with increased influenza in the following winter influenza season (expected 16% or 22% increase in influenza rate per 1 μg/m3 increase in average daily summer PM2.5 based on two analyses, p = 0.04 or 0.008). This is one of the first observations of a relationship between PM2.5 during wildfire season and influenza months later.

Chemical Composition of PM2.5 and Its Impact on Inhalation Health Risk Evaluation in a City with Light Industry in Central China

A city with light industry in China was selected for the study of the chemical characteristics of PM2.5 and to assess its impact on inhalation health risks. During the period from May 2017 to February 2018, a total of 382 PM2.5 filter samples were collected across four seasons (15–20 days for each season). The results showed that the daily average PM2.5 concentration ranged from 21 to 255 µg/m3, with an annual average of 73 ± 49 µg/m3. SO42−, NO3−, NH4+, and organic matter (OM) were the dominant components, accounting for 13%, 20%, 11%, and 20% of annual PM2.5 mass loading, respectively. Compared with the clean periods, the meteorology of the pollution periods were mostly characterized by high relatively humidity, high temperature, and low wind speeds. Based on positive matrix factorization (PMF), the major source of PM2.5 was identified as secondary aerosols, contributing 28% and 49% on clean days and polluted days, respectively. The health risk assessment of heavy metals showed that non-carcinogenic hazard is not expected to occur, while Cr contributed the highest cancer risk. This study is helpful for the advancement of our scientific understanding of PM2.5 pollution and its impact on health in cities with light industries.

Acute associations between PM2.5 and ozone concentrations and asthma exacerbations among patients with and without allergic comorbidities.

Acute effects of outdoor air pollution on asthma exacerbations may vary by asthma phenotype (allergic vs nonallergic). Associations of ambient PM2.5 and ozone concentrations with acute asthma visits (office, urgent, emergency, and hospitalization) were investigated using electronic medical records. International Classification of Disease codes were used to identify asthmatics, and classify them based on the presence or absence of an allergic comorbidity in their medical records. Daily 24-h average PM2.5, 8-h maximum ozone, and mean temperature were obtained from a centralized monitor. Using a time-stratified case-crossover approach, pollutant concentrations were modeled using moving averages and distributed lag nonlinear models (lag 0-6) to examine lag associations and nonlinear concentration-response. The adjusted odds ratios for a 10 µg/m3 increase in 3-day moving average (lag 0-2) PM2.5 in the two-pollutant models among patients with and without allergic comorbidities were 1.10 (95% confidence interval [CI]: 1.07, 1.13) and 1.05 (95% CI: 1.02, 1.09), respectively; and for a 20 ppb increase in 3-day moving average (lag 0-2) ozone were 1.08 (95% CI: 1.02, 1.14) and 1.00 (95% CI: 0.95, 1.05), respectively. Estimated odds ratios among patients with allergic comorbidities were consistently higher across age, sex, and temperature categories. Asthmatics with an allergic comorbidity may be more susceptible to ambient PM2.5 and ozone.

Analysis of Water Soluble Organic Aerosol in Spring PM2.5 with Soot Particle Aerosol Mass Spectrometry (SP-AMS)

To investigate the chemical composition and pollution characteristics of spring fine particles (PM2.5) in Changzhou, a total of 84 PM2.5 samples were collected from March 1st to May 30th, 2017. We measured and analyzed conventional components, such as water-soluble ions (WSIIs) and carbonaceous components (OC and EC). The water-soluble organic aerosol (WSOA) was also analyzed by an aerodyne soot particle aerosol mass spectrometer (SP-AMS). During the sampling period, the average daily PM2.5 concentration was 101.97 μg·m-3, with more than 73.8% sampling days exceeding the Target-2 standard of the national ambient air quality standard of China. The air quality during the sampling period was dominated by light, moderate, and heavy pollution, accounting for 39.3%, 21.4%, and 13.1% of the total days, respectively. The total WSIIs accounted for 39.86% of PM2.5 mass, of which secondary ions (SO42-, NH4+, and NO3-) accounted for 81.85% of the total WSIIs. The slope of the linear fitted line of the anion and cation charge balance (AE/CE) was greater than 1 (1.09), which indicated that PM2.5 was weakly acidic. The average OC/EC ratio was 2.53, indicating that PM2.5 was influenced by the secondary conversion. WSOA included CxHy+(32.1%), CxHyO+(30.4%), CxHyO2+(25.4%), and HyO+(4.7%) identified by SP-AMS. The average oxygen-to-carbon (O/C), hydrogen-to-carbon (H/C), nitrogen-to-carbon (N/C), and organic matter-to-organic carbon (OM/OC) ratios of the WSOA were 0.72, 1.53, 0.04, and 2.15, respectively. Higher O/C indicated higher contributions from secondary photochemical reaction conversion in spring. Positive matrix factorization (PMF) analysis for AMS mass spectra of WSOA identified three sources, namely hydrocarbon-like (HOA), semi-volatile oxygenated OA (SVOOA)-biomass burning OA (BBOA), and low-volatility oxygenated OA (LVOOA), which on average accounted for 18.4%, 34.1%, and 47.4% of the total WSOA, respectively.

Short-Term Associations of Fine Particulate Matter and Synoptic Weather Types with Cardiovascular Mortality: An Ecological Time-Series Study in Shanghai, China.

Background: Exposures to both ambient fine particulate matter (PM2.5) and extreme weather conditions have been associated with cardiovascular disease (CVD) deaths in numerous epidemiologic studies. However, evidence on the associations with CVD deaths for interaction effects between PM2.5 and weather conditions is still limited. This study aimed to investigate associations of exposures to PM2.5 and weather conditions with cardiovascular mortality, and further to investigate the synergistic or antagonistic effects of ambient air pollutants and synoptic weather types (SWTs). Methods: Information on daily CVD deaths, air pollution, and meteorological conditions between 1 January 2012 and 31 December 2014 was obtained in Shanghai, China. Generalized additive models were used to assess the associations of daily PM2.5 concentrations and meteorological factors with CVD deaths. A 15-day lag analysis was conducted using a polynomial distributed lag model to access the lag patterns for associations with PM2.5. Results: During the study period, the total number of CVD deaths in Shanghai was 59,486, with a daily mean of 54.3 deaths. The average daily PM2.5 concentration was 55.0 µg/m3. Each 10 µg/m3 increase in PM2.5 concentration was associated with a 1.26% (95% confidence interval (CI): 0.40%, 2.12%) increase in CVD mortality. No SWT was statistically significantly associated with CVD deaths. For the interaction between PM2.5 and SWT, statistically significant interactions were found between PM2.5 and cold weather, with risk for PM2.5 in cold dry SWT decreasing by 1.47% (95% CI: 0.54%, 2.39%), and in cold humid SWT the risk decreased by 1.45% (95% CI: 0.52%, 2.36%). In the lag effect analysis, statistically significant positive associations were found for PM2.5 in the 1–3 lag days, while no statistically significant effects were found for other lag day periods. Conclusions: Exposure to PM2.5 was associated with short-term increased risk of cardiovascular deaths with some lag effects, while the cold weather may have an antagonistic effect with PM2.5. However, the ecological study design limited the possibility to identify a causal relationship, so prospective studies with individual level data are warranted.

Spatiotemporal mixed effects modeling for the estimation of PM2.5 from MODIS AOD over the Indian subcontinent

ABSTRACTThe physical processes associated with the constituents of the troposphere, such as aerosols have an immediate impact on human health. This study employs a novel method to calibrate Aerosol Optical Depth (AOD) obtained from the MODerate resolution Imaging Spectrometer (MODIS – Terra satellite) for estimating surface PM2.5 concentration. The Combined Deep Blue Deep Target daily product from the MODIS AOD data acquired across the Indian Subcontinent was used as input, and the daily averaged PM2.5pollution level data obtained from 33 monitoring stations spread across the country was used for calibration. Mixed Effect Models (MEM) is a linear model to deal with non-independent data from multiple levels or hierarchy using fixed and random effects of dependent parameters. MEM was applied to the dataset obtained for the period from January to August 2017. The MEM considers a fixed and random component, where the random components model the daily variations of the AOD – PM2.5 relationships, site-specific adjustment parameters, temporal (meteorological) variables such as temperature, and spatial variables such as the percentage of agricultural area, forest cover, barren land and road density with the resolution of 10 km × 10 km. Estimation accuracy was improved from an R2 value of 0.66 from our earlier study (when PM2.5 was modeled against only AOD and site-specific parameters) toR2 value of 0.75 upon the inclusion of spatiotemporal (meteorological) variables with increased % within Expected Error from 18% to 35%, reduced Mean Bias Error from 3.22 to 0.11 and reduced RMSE from 29.11 to 20.09. We also found that spline interpolation performed better than IDW and Kriging inefficiently estimating the PM2.5 concentrations wherever there were missing AOD data. The estimated minimum PM2.5 is 93 ± 25μg/m3 which itself is in the upper limit of the hazardous level while the maximum is estimated as 170 ± 70μg/m3. The study has thus made it possible to determine the daily spatial variations of PM2.5 concentrations across the Indian subcontinent utilizing satellite-based AOD data.

Mortality associated with wildfire smoke exposure in Washington state, 2006\u20132017: a case-crossover study

BackgroundWildfire events are increasing in prevalence in the western United States. Research has found mixed results on the degree to which exposure to wildfire smoke is associated with an increased risk of mortality.MethodsWe tested for an association between exposure to wildfire smoke and non-traumatic mortality in Washington State, USA. We characterized wildfire smoke days as binary for grid cells based on daily average PM2.5 concentrations, from June 1 through September 30, 2006–2017. Wildfire smoke days were defined as all days with assigned monitor concentration above a PM2.5 value of 20.4 μg/m3, with an additional set of criteria applied to days between 9 and 20.4 μg/m3. We employed a case-crossover study design using conditional logistic regression and time-stratified referent sampling, controlling for humidex.ResultsThe odds of all-ages non-traumatic mortality with same-day exposure was 1.0% (95% CI: − 1.0 - 4.0%) greater on wildfire smoke days compared to non-wildfire smoke days, and the previous day’s exposure was associated with a 2.0% (95% CI: 0.0–5.0%) increase. When stratified by cause of mortality, odds of same-day respiratory mortality increased by 9.0% (95% CI: 0.0–18.0%), while the odds of same-day COPD mortality increased by 14.0% (95% CI: 2.0–26.0%). In subgroup analyses, we observed a 35.0% (95% CI: 9.0–67.0%) increase in the odds of same-day respiratory mortality for adults ages 45–64.ConclusionsThis study suggests increased odds of mortality in the first few days following wildfire smoke exposure. It is the first to examine this relationship in Washington State and will help inform local and state risk communication efforts and decision-making during future wildfire smoke events.

Bushfires in Australia: a serious health emergency under climate change

Bushfires in Australia: a serious health emergency under climate change

Prediction of Potentially High PM2.5 Concentrations in Chengdu, China

Daily exposure to high ambient PM2.5 increases the mortality rate and contributes significantly to the burden of disease. In basin-situated cities with high local emissions of air pollutants, meteorological conditions play a crucial role in forming air pollution. One such city is Chengdu, which is located in the Sichuan Basin and serves as the economic, educational, and transportation hub of western China. Particulate matter with an aerodynamic diameter of < 2.5 µm (PM2.5) is the most critical pollutant in this city. Although the annually averaged PM2.5 concentrations declined from 92 to 57 µg m–3 between 2013 and 2017, the city still suffers from haze and smog, with 85 days during 2017 displaying 24-h PM2.5 concentrations > 75 µg m–3. To better understand the influence of meteorological factors on PM2.5 pollution with the goal of easily and reliably predicting the latter, we examined the relationships between the 24-h concentration and a variety of meteorological parameters in Chengdu. We found that the strongest predictors of the PM2.5 concentration were the temperature, precipitation, wind speed, and trajectory direction and distance. Furthermore, although the same-day sea-level pressure (SLP) was a weak predictor, the SLP 5 days in advance performed better. We developed generalized additive models (GAMs) that predicted the PM2.5 concentration as a function of multiple meteorological parameters. One of the GAMs developed in this study exhibited an adjusted correlation coefficient (R2) of 0.73 and captured up to 73.9% of the variance in the daily averaged PM2.5 concentrations. The model performance was improved by using the ΔSLP (i.e., mean pressure difference) for 5 days instead of the SLP, suggesting that ΔSLP5d is a good predictor of high concentration days in Chengdu. This study provides a useful tool for controlling emissions in advance to prevent heavy pollution days and issuing outdoor activity warnings to protect public health.