High Levels Of PM2 Research Articles

Hypertension and Stroke as Mediators of Air Pollution Exposure and Incident Dementia

Fine particulate matter air pollution (PM2.5) has been consistently associated with cardiovascular disease, which, in turn, is associated with an increased risk of dementia. As such, vascular dysfunction might be a mechanism by which PM2.5 mediates dementia risk, yet few prior epidemiological studies have examined this potential mechanism. To investigate whether hypertension and stroke serve as mediators and modifiers of the association of PM2.5 with incident dementia. As part of the Environmental Predictors of Cognitive Health and Aging (EPOCH) Project, this cohort study used biennial survey data collected between 1998 and 2016 from respondents of the Health and Retirement Study (HRS), a nationally representative, population-based, cohort in the US. Eligible participants were those over 50 years of age who were free of dementia at baseline and had complete exposure, mediator, outcome, and demographic data from the HRS. Data analysis was conducted from August to November 2022. Exposure to PM2.5, calculated for the 10 years preceding each person's baseline examination according to residential histories and spatiotemporal models. Incident dementia was identified using a validated algorithm based on cognitive testing and informant reports. The 4-way decomposition causal mediation analysis method was used to quantify the degree to which hypertension and stroke mediated or modified the association of PM2.5 with incident dementia after adjustment for individual-level and area-level covariates. Among 27 857 participants (mean [SD] age at baseline, 61 [10] years; 15 747 female participants [56.5%]; 19 249 non-Hispanic White participants [69.1%]), 4105 (14.7%) developed dementia during the follow-up period (mean [SD], 10.2 [5.6] years). Among participants with dementia, 2204 (53.7%) had a history of hypertension at baseline and 386 (9.4%) received a diagnosis of hypertension during the follow up. A total of 378 participants (9.2%) had a history of stroke at baseline and 673 (16.4%) developed stroke over the follow-up period. The IQR of baseline PM2.5 concentrations was 10.9 to 14.9 μg/m3. In fully adjusted models, higher levels of PM2.5 (per IQR) were not associated with increased risk of incident dementia (HR, 1.04; 95% CI, 0.98 to 1.11). Although there were positive associations of prevalent stroke (HR, 1.67; 95% CI, 1.48 to 1.88) and hypertension (HR, 1.15; 95% CI, 1.08 to 1.23) with incident dementia compared with those free of stroke and hypertension during follow-up, there was no statistically significant association of PM2.5 with stroke (odds ratio per IQR increment in PM2.5, 1.08; 95%CI, 0.91 to 1.29) and no evidence of an association of PM2.5 with hypertension (odds ratio per IQR increment in PM2.5, 0.99; 95%CI, 0.92 to 1.07). Concordantly, there was no evidence that hypertension or stroke acted as mediators or modifiers of the association of PM2.5 with incident dementia. Although the nonmediated interaction between PM2.5 and hypertension accounted for 39.2% of the total excess association (95% CI, -138.5% to 216.9%), the findings were not statistically significant. These findings suggest that although hypertension may enhance the susceptibility of individuals to air pollution, hypertension and stroke do not significantly mediate or modify the association of PM2.5 with dementia, indicating the need to investigate other pathways and potential mediators of risk.

Using a novel strategy to investigate the spatially autocorrelated and clustered associations between short-term exposure to PM2.5 and mortality and the attributable burden: A case study in the Sichuan Basin, China

Due to the lack of statistical methods, few studies have investigated the spatial autocorrelated distribution in the association between short-term exposure to PM2.5 and mortality and used a statistical manner to explore the association-clustered regions, which play important roles in identifying high-sensitivity/susceptibility regions. The Sichuan Basin (SCB) is one of the most PM2.5-polluted areas, and the extreme economic imbalance may cause considerable spatial heterogeneity and clustering in PM2.5-mortality association. In this work, we used a recently proposed strategy by us to investigate the spatially autocorrelated and clustered association between daily PM2.5 and cardiorespiratory mortality from 2015 to 2019 in 130 counties of the SCB. First, generalized additive models were independently constructed to obtain the county-level association estimations. Then, an estimation-error-based spatial scan statistic was used to detect the association-clustered regions. Third, multivariate conditional meta autoregression was used to obtain the spatially autocorrelated association distribution, based on which the attributable deaths were mapped and their inequality was evaluated using the Gini coefficient and Lorenz curve. Results showed that two significantly association-clustered regions were detected. One is mainly located in the megacity Chengdu where PM2.5 presented a significantly stronger association with no threshold effect at low-level PM2.5 but a threshold at high-level PM2.5. In the other cluster, a threshold effect at low-level PM2.5 but no threshold at high-level PM2.5 were found. The mortality risk at low/middle-level PM2.5 decreased from Chengdu as the center to the surrounding areas. A total of 29,129 (2.0 %) deaths were attributable to the excess PM2.5 exposure. The attributable deaths also decreased from Chengdu as the center to the surrounding areas with Gini coefficients of 0.43 and 0.3 for absolute and relative attributable deaths, respectively. This novel strategy provided a new epidemiological perspective regarding the association and implicated that Chengdu is significantly deserving of more attention regarding PM2.5-related health loss.

Association Between the Combined Effects of Physical Activity Intensity and Particulate Matter and All-Cause Mortality in Older Adults

ObjectiveTo investigate the association between the combined effects of physical activity (PA) intensity and particulate matter ≤10 μm in diameter (PM10) and mortality in older adults. MethodsThis nationwide cohort study included older adults without chronic heart or lung disease who engaged in regular PA. Physical activity was assessed by a standardized, self-reported questionnaire that asked the usual frequency of PA sessions with low (LPA), moderate (MPA), or vigorous intensity (VPA). Each participant’s annual average cumulative PM10 was categorized as low to moderate and high PM10 on the basis of a cutoff value of 90th percentile. ResultsA total of 81,326 participants (median follow-up, 45 months) were included. For participants engaged in MPA or VPA sessions, every 10% increase in the proportion of VPA to total PA sessions resulted in a 4.9% (95% CI, 1.0% to 9.0%; P=.014) increased and 2.8% (95% CI, −5.0% to −0.5%; P=.018) decreased risk of mortality for those exposed to high and low to moderate PM10, respectively (Pinteraction, <.001). For participants engaged only in LPA or MPA sessions, every 10% increase in the proportion of MPA to total PA sessions resulted in a 4.8% (95% CI, −8.9% to −0.4%; P=.031) and 2.3% (95% CI, −4.2% to −0.3%; P=.023) decreased risk of mortality for those exposed to high and low to moderate PM10, respectively (Pinteraction, .096). ConclusionWe found that for the same level of total PA, MPA was associated with delayed mortality whereas VPA was associated with hastened mortality of older adults in high levels of PM10.

Evaluation of evidence for interaction between PM2.5 and aeroallergens on childhood asthma exacerbation in Philadelphia, PA, 2011 to 2016

Fine particulate matter (PM2.5) and aeroallergens (i.e., pollen, molds) are known triggers of asthma exacerbation. Despite mechanistic evidence suggesting synergistic effects between PM2.5 and asthma exacerbation, little epidemiologic work has been performed in children, which has exhibited inconsistency. We conducted a time-series study to explore their interactions using electronic health records (EHR) data in Philadelphia, PA, for asthma diagnoses in outpatient, emergency department [ED], and inpatient settings. Daily asthma exacerbation cases (28,540 asthma exacerbation case encounters) were linked to daily ambient PM2.5 and daily aeroallergen levels during the aeroallergen season of a six-year period (mid-March to October 2011–2016). Asthma exacerbation counts were modeled using quasi-Poisson regression, where PM2.5 and aeroallergens were fitted with distributed lag non-linear functions (lagged from 0 to 14-days), respectively, when modeled as the primary exposure variables. Regression models were adjusted for mean daily temperature/relative humidity, long-term and seasonal trends, day-of-week, and major U.S. holidays. Increasing gradient of RR estimates were observed for only a few primary exposure risk factors [PM2.5 (90th vs. 5th percentile)/aeroallergens (90th percentile vs. 0)], across different levels of effect modifiers. For example, RRs for the association between late-season grass pollen (lag1) and asthma exacerbation were higher at higher levels of PM2.5, 5-days preceding the exacerbation event (low PM2.5: RR = 1.01, 95% CI: 0.93–1.09; medium PM2.5: 1.04, 95% CI: 0.96–1.12; high PM2.5: 1.09, 95% CI: 1.01–1.19). However, most of the highest RRs for aeroallergens were instead observed for days with low- or medium- PM2.5 levels; likewise, when PM2.5 was modeled as the primary exposure with aeroallergens as the effect modifier. Most of the RR estimates did not exhibit gradients that suggested synergism, and were of relatively high imprecision. Overall, our study suggested no evidence for interactions between PM2.5 and aeroallergens in their relationships with childhood asthma exacerbation.

Grill workers and air pollution health effects from charcoal combustion in Vientiane capital

Introduction: Grilled street foods are popular in urban communities in Lao People's Democratic Republic (Lao PDR). Charcoal is the main fuel used for, posing a risk of elevated exposure to toxic pollutants. This study explored levels of cooking-related pollutants from grilled food business and workers’ health effects.&#x0D; Materials and methods: A quantitative approach using multiple techniques was conducted during March and April 2022 in Vientiane Capital, Lao PDR. Methods included pollutant emission estimation from charcoal-combusting grill shops/street-carts and Particulate Matter (PM2.5) measurement, and examined the exposure and health effects among grill workers. Multiple sampling techniques were applied to identify study samples. Respiratory symptoms were the health effect of interest among grill workers.&#x0D; Results: Estimated emission of pollutants was over 75 tons/year from grill shops. Average PM 2.5 level was 84.8 μg/m3 (21.6 - 254.8 μg/m3); which i above standard limits. A very high level of PM2.5 was found in grill markets. Most grill workers were female, worked 6-7 days/week, at least 8 h/day. Factors contributing to the presence of respiratory symptoms among grill workers were female gender, low income, indoor grilling, more years of grill-work, experience of intense smoke-cough, self-reliance on health and cigarette smoking.&#x0D; Conclusion: Grilling contributes to ambient air pollution, posing potential adverse environmental and public health impacts. Grill workers are likely to be exposed to high levels of all forms of air pollutants from street food grilling. Effective strategies are required to better protect grill workers from the effect of exposure to these harmful toxins and minimize the negative impacts on their health.

Association between ambient air pollutant interaction with kidney function in a large Taiwanese population study.

The associations and interactions between kidney function and other air pollutants remain poorly defined. Therefore, the aim of this study was to evaluate associations among air pollutants, including particulate matter (PM) with a diameter ≤ 2.5μm (PM2.5), PM10 (PM with a diameter ≤ 10μm), carbon monoxide (CO), nitrogen oxide (NO), nitrogen oxides (NOx), sulfur dioxide (SO2), and ozone (O3) with kidney function, and explore interactions among these air pollutants on kidney function. We used the Taiwan Air Quality Monitoring and Taiwan Biobank databases to derive data on community-dwelling individuals in Taiwan and daily air pollution levels, respectively. We enrolled 26,032 participants. Multivariable analysis showed that high levels of PM2.5, PM10, O3 (all p < 0.001), and SO2 (p = 0.001) and low levels of CO, NO (both p < 0.001), and NOx (p = 0.047) were significantly correlated with low estimated glomerular filtration rate (eGFR). With regard to negative effects, the interactions between PM2.5 and PM10 (p < 0.001), PM2.5 and PM10 (p < 0.001), PM2.5 and SO2, PM10 and O3 (both p = 0.025), PM10 and SO2 (p = 0.001), and O3 and SO2 (p < 0.001) on eGFR were significantly negatively. High PM10, PM2.5, O3, and SO2 were associated with a low eGFR, whereas high CO, NO, and NOx were associated with a high eGFR. Furthermore, negative interactions between PM2.5 and PM10, O3 and SO2, PM10 and O3, PM2.5 and SO2, and PM10 and SO2 on eGFR were observed. The findings of this study have important implications for public health and environmental policy. Specifically, the results of this study may be useful in individuals and organizations to take action to reduce air pollution and promote public health.

The effect of social determinants of health on severity of microbial keratitis presentation at a tertiary eye care hospital in Southern India.

Understanding the association between social determinants of health (SDoHs) and microbial keratitis (MK) can inform underlying risk for patients and identify risk factors associated with worse disease, such as presenting visual acuity (VA) and time to initial presentation. This was a cross-sectional study was conducted with patients presenting with MK to the cornea clinic at a tertiary care hospital in Madurai, India. Patient demographics, SDoH survey responses, geographic pollution, and clinical features at presentation were collected. Descriptive statistics, univariate analysis, multi-variable linear regression models, and Poisson regression models were utilized. There were 51 patients evaluated. The mean age was 51.2 years (SD = 13.3); 33.3% were female and 55% did not visit a vision center (VC) prior to presenting to the clinic. The median presenting logarithm of the minimum angle of resolution (logMAR) VA was 1.1 [Snellen 20/240, inter-quartile range (IQR) = 20/80 to 20/4000]. The median time to presentation was 7 days (IQR = 4.5 to 10). The average particulate matter 2.5 (PM2.5) concentration, a measure of air pollution, for the districts from which the patients traveled was 24.3 μg/m3 (SD = 1.6). Age- and sex-adjusted linear regression and Poisson regression results showed that higher levels of PM2.5 were associated with 0.28 worse presenting logMAR VA (Snellen 2.8 lines, P = 0.002). Patients who did not visit a VC had a 100% longer time to presentation compared to those who did (incidence rate ratio = 2.0, 95% confidence interval = 1.3-3.0, P = 0.001). Patient SDoH and environmental exposures can impact MK presentation. Understanding SDoH is important for public health and policy implications to mitigate eye health disparities in India.

Morphology and health risk assessment of potential toxic elements in size segregated PM at traffic intersection in Northern India

The current research attempted to evaluate the concentrations of PM in size segregation by analysing metal content and its health effect on human beings at the traffic junction. Higher PM levels (PM2.5–1.0 and PM1.0–0.5) were observed (418.55 µg/m3, 442.65 µg/m3) during the winter season while during the monsoon season, it was recorded as (75.54 µg/m3, 65.64 µg/m3). Overall metal concentrations for PM2.5–1.0 and PM1.0–0.5 were reported to be (39.01 µg/m3) and (29.84 µg/m3) during the winter season. A morphological study was also evaluated with source identification. Al, Ca, Fe, Mg, and Mn were found crustal source elements, whereas Cd, Cr, Cu, Zn, and Pb were observed to be highly enriched. The hazard Quotient for values was found higher in comparison with the safe limit which has adverse health effects on human health. 1.95 HQ values were observed for PM1.0–0.5 whereas 1.30 was found for PM2.5–1.0 respectively, which showed worry from smaller particles to humans. Adults' Lifetime cancer risk (ELCR) was found to be greater (0.0007) than children's (0.0002). The metal trend for carcinogenicity in both segments (PM2.5–1.0 and PM1.0–0.5) was Cr(VI)>Pb>Ni. Higher values of HQ and ELCR were found as PM1.0–0.5 >PM2.5–1.0. The average carcinogenic risk factor value revealed concentrations to be two times more than the USEPA's authorized level (1 × 10−6) smaller particles, according to the data, are more harmful or have a higher value for HQ and ELCR values.

A yearlong monitoring campaign of polycyclic aromatic compounds and other air pollutants at three sites in Sweden: Source identification, in vitro toxicity and human health risk assessment

Air pollution is a complex mixture of gases and particulate matter (PM) with local and non-local emission sources, resulting in spatiotemporal variability in concentrations and composition, and thus associated health risks. To study this in the greater Stockholm area, a yearlong monitoring campaign with in situ measurements of PM10, PM1, black carbon, NOx, O3, and PM10-sampling was performed. The locations included an Urban and a Rural background site and a Highway site. Chemical analysis of PM10 was performed to quantify monthly levels of polycyclic aromatic compounds (PACs), which together with other air pollution data were used for source apportionment and health risk assessment. Organic extracts from PM10 were tested for oxidative potential in human bronchial epithelial cells. Strong seasonal patterns were found for most air pollutants including PACs, with higher levels during the winter months than summer e.g., highest levels of PM10 were detected in March at the Highway site (33.2 μg/m3) and lowest in May at the Rural site (3.6 μg/m3). In general, air pollutant levels at the sites were in the order Highway > Urban > Rural. Multivariate analysis identified several polar PACs, including 6H-Benzo[cd]pyren-6-one, as possible discriminatory markers for these sites. The main sources of particulate pollution for all sites were vehicle exhaust and biomass burning emissions, although diesel exhaust was an important source at the Highway site. In vitro results agreed with air pollutant levels, with higher oxidative potential from the winter samples. Estimated lung cancer cases were in the order PM10 > NO2 > PACs for all sites, and with less evident seasonal differences than in vitro results. In conclusion, our study presents novel seasonal data for many PACs together with air pollutants more traditionally included in air quality monitoring. Moreover, seasonal differences in air pollutant levels correlated with differences in toxicity in vitro.

Impact of lifetime air pollution exposure patterns on the risk of chronic disease

Long-term exposure to air pollution can lead to cardiovascular disease, metabolic syndrome, and chronic respiratory disease. However, from a lifetime perspective, the critical period of air pollution exposure in terms of health risk is unknown. This study aimed to evaluate the impact of air pollution exposure at different life stages.The study participants were recruited from community centers in Northern Taiwan between October 2018 and April 2021. Their annual averages for fine particulate matter (PM2.5) exposure were derived from a national visibility database. Lifetime PM2.5 exposures were determined using residential address information and were separated into three stages (<20, 20–40, and >40 years). We employed exponentially weighted moving averages, applying different weights to the aforementioned life stages to simulate various weighting distribution patterns. Regression models were implemented to examine associations between weighting distributions and disease risk. We applied a random forest model to compare the relative importance of the three exposure life stages. We also compared model performance by evaluating the accuracy and F1 scores (the harmonic mean of precision and recall) of late-stage (>40 years) and lifetime exposure models.Models with 89% weighting on late-stage exposure showed significant associations between PM2.5 exposure and metabolic syndrome, hypertension, diabetes, and cardiovascular disease, but not gout or osteoarthritis. Lifetime exposure models showed higher precision, accuracy, and F1 scores for metabolic syndrome, hypertension, diabetes, and cardiovascular disease, whereas late-stage models showed lower performance metrics for these outcomes.We conclude that exposure to high-level PM2.5 after 40 years of age may increase the risk of metabolic syndrome, hypertension, diabetes, and cardiovascular disease. However, models considering lifetime exposure showed higher precision, accuracy, and F1 scores and lower equal error rates than models incorporating only late-stage exposures. Future studies regarding long-term air pollution modelling are required considering lifelong exposure pattern. .1

Chemical and morphological characterization by SEM–EDS of PM2.5 collected during winter in Ulaanbaatar, Mongolia

In this study, the morphological and elemental properties of airborne fine particles (PM2.5) collected during winter (15 December 2020–14 January 2021) in Ulaanbaatar, Mongolia, were investigated using scanning electron microscopy (SEM) combined with energy-dispersive X-ray spectrometry (EDS). SEM analysis of the PM2.5 samples revealed that the particle shape distribution on haze days (daily mean PM2.5 concentration of over 100 μg m−3) was irregular (62%), spherical (24.6%), cluster (10.7%), and chain-like (2.7%), while the particle shape on clean days (daily mean PM2.5 concentration of less than 30 μg m−3) was distributed as follows: irregular (56%), cluster (22%), spherical (17.3%), and chain-like (4.7%). The apparent mean particle size on haze days (2.07 μm) was twofold greater than that on clean days (1.13 μm). Based on the EDS spectra, carbonaceous particles were the most abundant (38%), followed by mineral dust (36%), Fe-rich particles (4.7%), N-rich particles (4.7%), calcium sulfate (4%), fly ash (4%) and others (2%) on clean days. On haze days, carbonaceous particles accounted for 86% of the total, and Fe-rich, mineral, transition metal, and calcium sulfate particles accounted for 6.7, 4.7, 1.3 and 0.7%, respectively, indicating that carbonaceous particles were the main contributor on haze days. In particular, the sample filters collected on haze days were predominantly covered with tar/soot-like sticky matter, in contrast to those collected on clean days. Spearman's rank correlation analysis of PM2.5 with inorganic gaseous components as well as meteorological conditions further revealed that high levels of PM2.5 in winter in Ulaanbaatar were significantly associated with SO2 (ρ = 0.95), and CO (ρ = 0.94). These associations indicated that ambient SO2 and CO gases are indicative of haze episodes during the study period, and suggested a strong contribution of solid fuel combustion producing those gases in Ulaanbaatar during winter.

Are associations of leisure-time physical activity with mortality attenuated by high levels of chronic ambient fine particulate matter (PM2.5) in older adults? A prospective cohort study

Background and purposeAlthough leisure-time physical activity (PA) has established health benefits in older adults, it is equivocal if exercising in environments with high levels of PM2.5 concentrations is equally beneficial for them. To explore the independent and joint associations of ambient PM2.5 and PA with all-cause mortality among adults aged 60 or older and to assess the modifying effect of age (60–74 years vs. 75+ years) on the joint associations. MethodsA prospective cohort study based on the MJ Cohort repeat examinations (2005–2016) and the Taiwan Air Quality Monitoring Network and death registry linkages (2005–2022). We included MJ Cohort participants aged 60 or more at baseline who attended the health check-ups at least twice (n = 21,760). Metabolic equivalent hours per week (MET-h/week) of leisure-time PA were computed. Multivariable adjusted associations were examined using time-varying Cox proportional hazard models. ResultsThere were 3539 all-cause deaths over a mean follow-up of 12.81 (SD = 3.67) years. Ambient PM2.5 and physical inactivity are both independently associated with all-cause mortality. The joint associations of PA and PM2.5 concentrations with all-cause mortality differed in the young-old (60–74 years) and the older-old (75+ years) (P for interaction = 0.01); Higher levels of long-term PM2.5 exposures (≥25 μg/m3) had little influence on the associations between PA and mortality in the young-old (HR = 0.68 (0.56–0.83) and HR = 0.72 (0.59–0.88) for participants with 7.5–<15 and 15+ MET-h/week respectively) but eliminated associations between exposure and outcome in the older-old (HR = 0.91 (0.69–01.21) and HR = 1.02 (0.76–1.38) for participants with 7.5–<15 and 15+ MET-h/week). ConclusionLong-term exposures to higher PM2.5 concentrations may eliminate the beneficial associations of PA with all-cause mortality among adults aged 75 and over.

Time Effects of Global Change on Forest Productivity in China from 2001 to 2017

With global warming, the concentrations of fine particulate matter (PM2.5) and greenhouse gases, such as CO2, are increasing. However, it is still unknown whether these increases will affect vegetation productivity. Exploring the impacts of global warming on net primary productivity (NPP) will help us understand how ecosystem function responds to climate change in China. Using the Carnegie-Ames-Stanford Approach (CASA) ecosystem model based on remote sensing, we investigated the spatiotemporal changes in NPP across 1137 sites in China from 2001 to 2017. Our results revealed that: (1) Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP) were significantly positively correlated with NPP (p < 0.01), while PM2.5 concentration and CO2 emissions were significantly negatively correlated with NPP (p < 0.01). (2) The positive correlation between temperature, rainfall and NPP gradually weakened over time, while the negative correlation between PM2.5 concentration, CO2 emissions and NPP gradually strengthened over time. (3) High levels of PM2.5 concentration and CO2 emissions had negative effects on NPP, while high levels of MAT and MAP had positive effects on NPP.

Monitoring of Spatio-Temporal Dynamics of Air Pollution in Enugu Urban using Remote Sensing and GIS

Ambient air pollution is widely acknowledged to be the worst type of environmental pollution. The amount of air pollution varies from time to time and from region to region. The spatiotemporal distribution of air pollutants in the city of Enugu was investigated in this study. Due to the high concentration of industrial clusters, heavy traffic and increasing population density in the study area, notable places were purposely sampled during the hours of 8a.m. and 4p.m. A portable gas analyzer, the Aerocet-531 Met One Instrument, Drager X-am 5000, was used to quantify the amounts of PM2.5, PM10, CO, SO2, and NO2. The GPS coordinates of the sampling spots were recorded using the portable Germin-300 Global Positioning System (GPS) device analyzer, which aided in the data processing to create spatial interpolation maps in ArcMap. GIS and remote sensing methods were utilized to analyze the spatial distribution of contaminants using satellite images. ArcGIS and Erdas Imagine were used to analyze the identification of pollution sources, the geographical distribution of pollutants in connection to land use and the epidemiological scope of pollutants from sources. The approaches employed to get the findings included image categorization, proximity, intersection, and interpolation. It was determined that there was a correlation between the landuse density and the levels of NO2, SO2, CO, PM2.5, and PM10. Within 100m – 400m of major roadways, correlation values were highest, indicating a high risk of vulnerability in this area. The maximum values for PM2.5 and PM10 were 1.78μg/m3 and 205μg/m3 respectively during the dry season and 2.13μg/m3 and 184μg/m3 during the wet weather. Similarly, the minimum values for PM2.5 and PM10 are 0.25μg/m3 and 161μg/m3 respectively during the dry season and 0.45μg/m3 and 132μg/m3 during the wet weather. The study further shows that the range values of SO2, NO2, and CO during the dry season are between 0.71 to 1.98ppb, 0.72 to 1.96ppb and 9.21 to 18.04ppb respectively while ranges of 0.45 to 1.73ppb, 0.31 to 0.89ppb, and 7.94 to 12.56ppb were recorded during the wet season. The study concluded that the high levels of PM2.5, PM10, SO2, NO2, and CO in the study area monitored especially in the dry season may present a potential public health risk. Sequel to this, it is advised that the study area should reduce their exposure in light of this, especially during the dry season.

Distribution and Meteorological Control of PM2.5 and Its Effect on Visibility in Northern Thailand

In the dry season, the north of Thailand always experiences reduced air quality, reduced visibility, and public health exposure from the burning of biomass domestically and in surrounding countries. The purpose of this research was to investigate the distribution and the meteorological control of PM2.5 accumulation, as well as its effect on visibility in northern Thailand in 2020. The Geographic Information System (GIS) was applied for the analysis of the spatial distribution, while Pearson’s correlation coefficient was utilized to examine the association between PM2.5 and meteorological variables. The results showed that the PM2.5 concentrations were in the range of 16–195 μg/m3 in 2020. The high level of PM2.5 in Lampang, Chiang Rai, and Chiang Mai provinces was in the range of 150 to 195 μg/m3 from January to May. Favorable meteorological conditions included low wind and relative humidity, and high temperatures contributed to high PM2.5 concentrations in northern Thailand. Domestic burning and burning in neighboring countries contribute to huge amounts of smoke that cause low visibility in northern Thailand, especially at 1 km above ground level, with a reduced visibility in the range of 70–90% for all provinces in April.

Heat-related cardiorespiratory mortality: Effect modification by air pollution across 482 cities from 24 countries

BackgroundEvidence on the potential interactive effects of heat and ambient air pollution on cause-specific mortality is inconclusive and limited to selected locations. ObjectivesWe investigated the effects of heat on cardiovascular and respiratory mortality and its modification by air pollution during summer months (six consecutive hottest months) in 482 locations across 24 countries. MethodsLocation-specific daily death counts and exposure data (e.g., particulate matter with diameters ≤ 2.5 µm [PM2.5]) were obtained from 2000 to 2018. We used location-specific confounder-adjusted Quasi-Poisson regression with a tensor product between air temperature and the air pollutant. We extracted heat effects at low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th percentile of the location-specific pollutant concentrations. Country-specific and overall estimates were derived using a random-effects multilevel meta-analytical model. ResultsHeat was associated with increased cardiorespiratory mortality. Moreover, the heat effects were modified by elevated levels of all air pollutants in most locations, with stronger effects for respiratory than cardiovascular mortality. For example, the percent increase in respiratory mortality per increase in the 2-day average summer temperature from the 75th to the 99th percentile was 7.7% (95% Confidence Interval [CI] 7.6–7.7), 11.3% (95%CI 11.2–11.3), and 14.3% (95% CI 14.1–14.5) at low, medium, and high levels of PM2.5, respectively. Similarly, cardiovascular mortality increased by 1.6 (95%CI 1.5–1.6), 5.1 (95%CI 5.1–5.2), and 8.7 (95%CI 8.7–8.8) at low, medium, and high levels of O3, respectively. DiscussionWe observed considerable modification of the heat effects on cardiovascular and respiratory mortality by elevated levels of air pollutants. Therefore, mitigation measures following the new WHO Air Quality Guidelines are crucial to enhance better health and promote sustainable development.

Forecasting high levels of PM&lt;SUB&gt;10&lt;/SUB&gt; in Korea based on the principal expectile component regression

As the level of fine dust has risen sharply recently, many studies has been conducted to analyze the data. Since exposure to fine dust is related to the occurrence of cardiovascular diseases and respiratory, it can make the mortality rate increase. Therefore, it is important to predict the extreme level of fine dust. In this paper, we consider a regression model based on the principal expectile analysis. Compare to the conventional principal component analysis, principal expectile analysis can capture variations around the tail of the data. By so doing, we predict ‘Bad’ cases of the PMSUB10/SUB level of 25 districts in Seoul, South Korea and compare the results with the classical principal component regression. From the results, we observe that the proposed model predicts the extreme level of fine dust better than the existing model.

Investigation of the Contents of the Stack Emissions of Iron Ore Sinter Plants With and Without Bag Filter.

This investigation aims to identify the reasons for the plumes' visibility, compare the stacks with other sinter plant stacks worldwide, and suggest countermeasures to completely stop the visibility of emissions. The appearance of the sinter plant stack emission changes with time and the background color of the sky due to the scattering effect of the sunlight and incidence angle. The flue gas samples were collected at the outlet of the emission control equipment and observed under optical and scanning electron microscopes. The characterization was performed with the help of an electron dispersive spectroscope and mapping technique. The contents of the stack of a sinter plant without a bag filter had much higher levels of PM10, SO2, and NOx. The emissions from all the sinter plants were invariably found to have particulates of SO2 and NOx of size less than 2.5 microns. It is suggested to opt for state-of-the-art fabric filter technology to eliminate PM2.5 emissions also.

Measuring Smoke Emissions from Prescribed Rangeland Burning in the Flint Hills Region Using Unmanned Aircraft Systems

Highlights Smoke samples collected with UAS during rangeland prescribed burns were used to develop representative smoke emission factors for rangeland fires. The head fire emission factors of PM2.5 and NOx were consistent with the low end of the range of flaming emission factors in the literature. The combination of high air temperatures and high absolute humidity resulted in higher levels of PM2.5 and O3 in smoke. Abstract.Prescribed burning is an ecological process critical to maintaining and improving rangeland ecosystems. Smoke impacts related to prescribed burning have been the subject of intense discussion and public debate. The objectives of this study were to collect accurate smoke emissions data using unmanned aircraft systems (UAS) and use the data to develop smoke emission factors representative of prescribed rangeland fires in the tallgrass prairie Flint Hills region of Kansas and Oklahoma. The emission factors will be used to improve the input parameters used in smoke modeling tools. Four prescribed burns on unique burn units were sampled for this study. Smoke emission factors were determined using the carbon mass balance method. Average emission factors for head fires were: PM2.5, 11.3±10.8g/kg fuel; NOx as NO, 1.4±0.9 g/kg fuel; CO2, 1569±28 g/kg fuel; CH4, 6.8±4.3 g/kg fuel; NMHC as propane, 3.3±2.5 g/kg fuel; and VOC as propane, 4.5±3.5 g/kg fuel. Compared with head fires, back fires tended to produce lower emissions of PM2.5 but higher emissions of NOx and VOC. Green, high-moisture vegetation present during the growing season fires in September resulted in a larger head fire PM2.5 emission factor compared to April fires. Generally, the combination of high air temperature and high absolute humidity resulted in high PM2.5 and O3 in smoke. Conducting prescribed fires under conditions of cool air temperature and low absolute humidity can reduce the generation of PM2.5 and O3 in smoke, as long as these conditions fall within the prescribed range for the burn. Keywords: Carbon mass balance, Drone, Fire, Grass, Ozone, Pasture, Prescribed burning, Rangeland management, Rangelands, Smoke emission factor, Tallgrass prairie, Unmanned aircraft system.

AIRO: Development of an Intelligent IoT-based Air Quality Monitoring Solution for Urban Areas

Air pollution is the contamination of the atmosphere by any biological, physical, or chemical means. Bengaluru, the Silicon Valley of India, has air pollution levels that exceed WHO standards. Its air has high PM10, PM2.5, SO2, NO2, and CO2 levels, exposing residents to an increased risk of respiratory diseases. AIRO, a decentralised IoT-based air quality monitoring solution, is proposed to calculate the (Air Quality Index) AQI in real-time and notify the users of dangerous AQI levels. Unlike the city's fixed air quality monitoring stations, this portable system can easily be integrated into everyday items for computing real-time air quality at any given place. The proposed solution is then validated using a physical prototype incorporated into a water bottle. Using the Intel Edison development platform, this prototype is equipped with a GPS module, Wi-Fi module, PMS5003, MQ131, MQ135, MQ136, MQ7 sensors, and an LCD Display. The prototype records the air quality indicators, calculates the AQI, and sends the data to the AWS Cloud Server. After an in-depth analysis of the cloud data, the daily and weekly AQI is predicted for multiple locations in the city. A hybrid CNN-Bi-LSTM model is proposed for predicting AQI, which needs to be evaluated at the city scale for dependable results. Finally, a smartphone app is developed using Android Studio and Python to monitor the air quality and notify the users. The users also have an option to select a location and get the real-time and predicted AQI. In the future, this hybrid deep-learning model can be extended to other cities using the transfer learning approach.