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The issue of fine particulate matter in Thailand, especially in Northern Thailand, is an urgent problem that needs to be solved because of potential harm to human health. Prior estimates of fine particulate matter help planning how to reduce it. The daily fine particulate matter data reports usually contain some missing values. An improved ratio estimator has been suggested for population total under unequal probability sampling without replacement. The improved estimator is studied under a reverse framework when the nonresponse mechanism is not uniform, called missing at random nonresponse, which is more convenient to apply in practice. The variance and its associated estimator are investigated in theory. Simulation studies are used to assess the suggested estimator's performance. The new estimator is also applied to estimate fine particulate matter in Northern Thailand. The results show that the suggested estimator under the missing at random nonresponse mechanism performs well, as opposed to the existing estimator under missing completely at random assumption. The estimated fine particulate matter levels in Northern Thailand from the proposed estimator give a lesser variance than the existing one. [ABSTRACT FROM AUTHOR]

In order to study the influence of typical coal-fired flue gas components on the supersaturation characteristics in a multisection growth tube, a two-dimensional heat and mass transfer model was used to predict the supersaturation profiles formed by the cool saturated flow into a warm-walled growth tube (Method 1) and the warm saturated flow into a cold-walled growth tube (Method 2). The calculated results show that the increase in the concentration of the three typical components CO2, SO2, and RH (relative humidity) of the flue gas is beneficial for the achievement of the supersaturated environment in Method 1. Additionally, having too many sections of the growth tube has a disadvantage in achieving the supersaturation profiles. When the content of the gas of lower Le is higher, having more sections of the growth tube is beneficial to achieving a supersaturated environment, while a lower number of sections is necessary when the gas content is lower in Method 2. In a word, the appropriate number of sections in a growth tube is significantly important for the achievement of a favorable supersaturated environment. [ABSTRACT FROM AUTHOR]

Few studies have explored the effects of fine particulate matter (PM2.5) and its constituents on the progression of cerebral blood flow velocity (BFV) and the potential modifying role of greenness. In this study, we investigated the association of PM2.5 and its constituents, including sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), organic matter (OM), and black carbon (BC), with the progression of BFV in the middle cerebral artery. Participants from the Beijing Health Management Cohort who underwent at least two transcranial Doppler sonography examinations during 2015–2020 were recruited. BFV change and BFV change rate were used to define the progression of cerebral BFV. Linear mixed effects models were employed to analyze the data, and the weighted quantile sum regression assessed the contribution of PM2.5 constituents. Additionally, greenness was examined as a modifier. Among the examined constituents, OM exhibited the strongest association with BFV progression. An interquartile range increase in PM2.5 and OM exposure concentrations was associated with a decrease of −16.519 cm/s (95% CI: −17.837, −15.201) and −15.403 cm/s (95% CI: −16.681, −14.126) in BFV change, and −10.369 cm/s/year (95% CI: −11.387, −9.352) and −9.615 cm/s/year (95% CI: −10.599, −8.632) in BFV change rate, respectively. Furthermore, stronger associations between PM2.5 and BFV progression were observed in individuals working in areas with lower greenness, those aged under 45 years, and females. In conclusion, reducing PM2.5 levels in the air, particularly the OM constituent, and enhancing greenness could potentially contribute to the protection of cerebrovascular health. Plain Language Summary: Fine particulate matter in the atmosphere has been shown to be associated with cerebrovascular disease, however, the adverse effects of fine particulate matter chemical constituents on cerebral hemodynamics and the role of greenness in this are yet to be evaluated. In this study, we evaluated the association of fine particulate matter and its constituents (sulfate, nitrate, ammonium, black carbon, and organic matter) with cerebral arterial blood flow velocity (BFV) and the modifying effect of greenness. We found that fine particulate matter and its constituents accelerate the reduction of cerebral arterial BFV and that the association is stronger where greenness levels are low. Our findings provide new evidence that reducing fine particle exposure and enhancing urban greenery may be an effective way to protect cerebrovascular health. Key Points: PM2.5 and its constituents were associated with a greater and faster reduction in the cerebral artery blood flow velocityThe impacts of PM2.5 and its constituents were modified by greennessYounger people and females were found to be more vulnerable [ABSTRACT FROM AUTHOR]

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Many studies have examined the associations between exposure to indoor and outdoor atmospheric particulate matter and health outcomes in humans. There is increasing evidence that the oxidative characteristics of such particulate matter have a role in determining the adverse health effects of fine particulates. Moreover, since people spend more time in indoors (office and home), indoor air quality and its impact on human health have increased in importance. However, to date, studies examining the oxidative characteristics of indoor particulates are few, and there has been a limited examination of the impact on physiological conditions of indoor fine particulates exposure on microorganisms. Therefore, this study aimed to investigate the oxidative characteristics of fine particulates in an indoor environment on the -3rd floor. The chemical characteristics of indoor PM2.5 samples were determined through their elemental composition, particle oxidation state, and organic and inorganic functional groups using EDX and FTIR spectrometry. Oxidative characteristics were also examined in terms of the cellular response of opportunistic bacteria (Staphylococcus aureus and Escherichia coli) by oxidative stress indicators (e.g., antioxidant, catalase, superoxide dismutase, glutathione (reduced), lipid peroxidation, and hydrogen peroxide), applied to artificial lung fluid and TRIS soluble fractions of aerosol that was extracted from the fine mode (PM2.5) of 120-h filter samples. Bacterial activity and protein content of S. aureus and E. coli were also studied in order to understand the main biochemical response of opportunistic bacteria. The chemical analysis results showed that elements in the indoor PM2.5 filters were both crustal origin (e.g., Al, Si, K, and Ca) and anthropogenic (e.g., Ba, Zn, and Ce). The identified functional groups were S = O, N–H, N = O, C = O, C-H, and O–H, which can cause oxidative stress. Bacteria-based oxidative indicators showed that both PM2.5 and physicological fluids induced the oxidative stress. However, oxidative responses were changed by the type of bacteria and physicological fluid, and PM2.5 was disturbed by the natural protection of physicological fluids. [ABSTRACT FROM AUTHOR]

Through the integration and analysis platform of particulate matter components, a preliminary comparison was conducted on the pollution contribution of the three winter transit air masses in December to environmental quality, and the changes and related relationships of water-soluble ions and particulate matter elements were analyzed. The results showed that winter transit air masses had a significant impact on the air quality in Hengyang. In the quantitative comparison of the primary pollutant contribution during the transit period of air masses, local sources and transit sources each accounted for half, and the impact of transit source on ambient air quality was much greater than that of seasonal base increase. Fine particulate matter was closely related to secondary ions, and particulate matter was closely related to primary ions. The transit of air masses promoted the improvement of secondary ion conversion rate, and the unit increment of fine particulate matter was greater than that of particulate matter. During the transit period, the mass concentrations of most water-soluble ions and most particulate matter elements maintained synchronous growth, with a superimposed effect. The composition ratio of organic carbon and nitrate ions increased, while the composition ratio of ammonia and sulfate ions decreased. Both disposable ions and secondary conversions in the composition of PM2 s had increases and decreases. The high conversion rate of nitrate and the high composition ratio of ammonia radical in Hengyang indicated that transportation source factors had a significant impact on the local environmental quality of Hengyang. [ABSTRACT FROM AUTHOR]

Airborne particulate matter (PM) is a vector of many toxic pollutants, including polyaromatic hydrocarbons (PAHs) and their derivatives. Especially harmful is the fine fraction (PM2.5), which penetrates deep into the lungs during inhalation and causes various diseases. Amongst PM2.5 components with toxic potential are nitrated PAHs (NPAHs), knowledge of which is still rudimentary. Three of the measured NPAHs (1-nitropyrene (1-nP), 9-nitroanthracene (9-nA), and 6-nitrochrysene (6-nC)) were detected in ambient PM2.5 from Ljubljana, Slovenia, along with thirteen non-nitrated PAHs. The highest concentrations of pollutants, which are closely linked with incomplete combustion, were observed in the cold part of the year, whereas the concentrations of NPAHs were roughly an order of magnitude lower than those of PAHs throughout the year. Further on, we have evaluated the toxicity of four NPAHs, including 6-nitrobenzo[a]pyrene (6-nBaP), to the human kidney cell line, HEK293T. The most potent was 1-nP (IC50 = 28.7 µM), followed by the other three NPAHs, whose IC50 was above 400 or 800 µM. According to our cytotoxicity assessment, atmospheric 1-nP is the most harmful NPAH among the investigated ones. Despite low airborne concentrations of NPAHs in ambient air, they are generally considered harmful to human health. Therefore, systematic toxicological assessment of NPAHs at different trophic levels, starting with cytotoxicity testing, is necessary in order to accurately evaluate their threat and adopt appropriate abatement strategies. [ABSTRACT FROM AUTHOR]

In recent years, pollution of atmospheric particulate matter has been ameliorated continuously in Wuhan, but PM2.5 is still the main pollutant affecting the ambient air quality. This paper reviewed the research on the source apportionment of fine particulate matter in Wuhan during the period of 2012-2022 with respect to the status quo of the research of PM2.5 sources in Wuhan, and the spatial and temporal distribution characteristics of PM2.5 sources were sorted out and summarized. The source apportionment of PM2.5 revealed that the relative contribution of secondary source and biomass combustion source increased, while the relative contribution of dust source declined a lot in recent years, with the traffic source remained stabilized in general. Furthermore, in future the research direction of the source apportionment atmospheric particulate matter in Wuhan should focus on the mutual verification of multiple models and methods, the traceability of secondary aerosols, and the real-time rapid source analysis in the process of heavy smog pollution. [ABSTRACT FROM AUTHOR]

Data were collected in 70 detached houses built in 2011-2017 in compliance with the mechanical ventilation requirements of California's building energy efficiency standards. Each home was monitored for a 1-week period with windows closed and the central mechanical ventilation system operating. Pollutant measurements included time-resolved fine particulate matter (PM2.5 ) indoors and outdoors and formaldehyde and carbon dioxide (CO2 ) indoors. Time-integrated measurements were made for formaldehyde, NO2 , and nitrogen oxides (NOX ) indoors and outdoors. Operation of the cooktop, range hood, and other exhaust fans was continuously recorded during the monitoring period. Onetime diagnostic measurements included mechanical airflows and envelope and duct system air leakage. All homes met or were very close to meeting the ventilation requirements. On average, the dwelling unit ventilation fan moved 50% more airflow than the minimum requirement. Pollutant concentrations were similar to or lower than those reported in a 2006-2007 study of California new homes built in 2002-2005. Mean and median indoor concentrations were lower by 44% and 38% for formaldehyde and 44% and 54% for PM2.5 . Ventilation fans were operating in only 26% of homes when first visited, and the control switches in many homes did not have informative labels as required by building standards.

Evidence suggests exposure to particulate matter with aerodynamic diameter

Abstract Background Considerable attention has been paid to reproductive toxicity of fine particulate matter (PM2.5). However, the relationship between prenatal PM2.5 exposure and anogenital distance (AGD) has not been well studied. We aim to investigate the potential effects of prenatal exposure to PM2.5 on newborn AGD. Methods Prenatal PM2.5 exposure of 2332 participates in Shanghai (2013–2016) was estimated using high-performance machine learning models. Anoscrotal distance (AGDas) in male infants and anofourchette distance (AGDaf) in female infants were measured by well-trained examiners within 3 days after birth. We applied multiple linear regression models and multiple informant models to estimate the association between prenatal PM2.5 exposure and AGD. Results Multiple linear regression models showed that a 10 μg/m3 increase in PM2.5 exposure during full pregnancy, the second and third trimesters was inversely associated with AGDas (adjusted beta = − 1.76, 95% CI: − 2.21, − 1.31; − 0.73, 95% CI: − 1.06, − 0.40; and − 0.52; 95% CI: − 0.87, − 0.18, respectively) in males. A 10 μg/m3 increase in PM2.5 exposure during the full pregnancy, the first, second, and third trimesters was inversely associated with AGDaf (adjusted beta = − 4.55; 95% CI: − 5.18, − 3.92; − 0.78; 95% CI: − 1.10, − 0.46; − 1.11; 95% CI: − 1.46, − 0.77; − 1.45; 95% CI: − 1.78, − 1.12, respectively) in females after adjusting for potential confounders. Multiple informant models showed consistent but slightly attenuated associations. Conclusion Our study observed a significant association between gestational PM2.5 exposure during pregnancy and shortened AGD in newborns, and provided new evidence on potential reproductive toxicity of prenatal PM2.5 exposure.

Limited research has been conducted on the contributions of local and nonlocal emission sources to ambient fine particulate matter (PM2.5) and ozone (O3) and their associated mortality. In this study, we estimated the total mortality resulting from long-term PM2.5 and O3 exposures in California in 2012 using multiple concentration response functions (CRFs) and attributed the estimated mortality to different emission groups. The point estimates of PM2.5-associated mortality in California ranged from 12,700 to 26,700, of which 53% were attributable to in-state anthropogenic emissions. Based on new epidemiological evidence, we estimated that O3 could be associated with up to 13,700 deaths from diseases of both the respiratory and cardiovascular systems in California. In addition, 75% of the ambient O3 in California was due to distant emissions outside the western United States, leading to 92% of the O3-associated mortality. Overall, distant emissions lead to greater mortality burdens of air pollution in California than local anthropogenic emissions.

This study aimed to conduct a meta-analysis to investigate whether short-term exposure to fine (PM2.5) and coarse (PM10) particulate matter was associated with acute exacerbation of chronic obstructive pulmonary disease (AECOPD) hospitalization, emergency room visit, and outpatient visit at different lag values. PubMed, Embase, and the Cochrane Library were searched for relevant papers published up to March 2021. For studies reporting results per 1-µg/m3 increase in PM2.5, the results were recalculated as per 10-µg/m3 increase. We manually calculated the RRs for these two studies and transferred the RRs to estimate 10 µg/m3 increases in PM2.5. Automation tools were initially used to remove ineligible studies. Two reviewers independently screened the remaining records and retrieved reports. Twenty-six studies (28 datasets; 7,018,419 patients) were included. There was a significant association between PM2.5 and AECOPD events on lag0 (ES = 1.01, 95%CI: 1.01-1.02, p

Air pollution is one of the main environmental problems in megacities, such as the metropolitan area of São Paulo (MASP), in Brazil. Urban forests can alleviate air pollution by providing a surface for the dry deposition of particles and trace gases. To benefit from this environmental service and design future green structures, it is crucial to estimate the removal rate of air pollutants by trees. The removal rates of fine particulate matter (PM2.5) by urban trees were quantitatively assessed for the first time in Brazil. A big-leaf modeling approach was adopted, using the i-Tree Eco software. In situ dendrometric data, hourly PM2.5 concentrations, and meteorological variables were used as inputs to the model. PM2.5 removal fluxes ranged between 0.06 and 0.21 g/m2/year in three study areas with contrasting urban forestry conditions. The neighborhood with the greatest canopy cover and tree diversity had the highest removal rates. The evergreen character of the urban forest in the MASP possibly contributed to the relatively high PM2.5 removal fluxes, as compared to other cities around the world. Removal rates were higher in the austral summer, when high precipitation rates restricted the resuspension of deposited particles back to the atmosphere. When extrapolated to the whole metropolitan area, assuming homogeneous forestry conditions, the estimated PM2.5 removal rates were comparable to the magnitude of vehicular emissions, showing that air pollution removal by trees can be substantial in the MASP. The results demonstrate the contribution of urban trees to the improvement of air quality and can boost the development of public policies on urban afforestation in the MASP.

BackgroundFine particulate matter (PM2.5) is a serious air pollutant associated with elevated levels of C-reactive protein (CRP), an inflammatory indicator.ObjectiveTo assess the potential impacts of long-term exposure to PM2.5 on CRP levels based on previous epidemiological studies.MethodsPubMed, Embase, Web of Science, CNKI, and Wanfang databases were searched to screen the cohort studies published from January 1, 2000 to January 1, 2022 on the effects of long-term exposure to PM2.5 on CRP levels. "Fine Particulate Matter", "PM2.5", "Particulate Air Pollutants", "Ambient Particulate Matter", "CRP", "C-reactive Protein", and "High Sensitivity C-reactive Protein" in English or Chinese were the key words used in the search. The percentage change in CRP level per 10 μg·m−3 increase in PM2.5 concentration in each study was extracted, followed by meta-analysis, subgroup analysis, and sensitivity analysis.ResultsA total of 1241 articles were retrieved, and 7 articles were included. Random-effects models were used to merge the included data, and it was found that the percentage of CRP level increased by 10.41% (95%CI: 2.24%-18.57%, P

Ambient air pollution has become a widespread global public health problem. As one of the main components of ambient air pollution, fine particulate matter (PM2.5), with its small diameter and large surface area, can carry a variety of toxic substances and enter the blood circulation directly through the blood-air barrier, damaging various tissues and organs of human body. Studies have shown that PM2.5 exposure during pregnancy can disrupt the mother's and child's thyroid function. Since the fetal thyroid gland does not begin to develop until around the sixth week of pregnancy, the fetal thyroid hormone is almost entirely dependent on the mother during early stages of pregnancy, and maternal thyroid hormone level play a crucial role in the growth and development of fetus. When a mother is exposed to PM2.5 during pregnancy, placenta, the "bridge" between mother and fetus, is also affected to some extent, including changes in placental iodine uptake and oxidative stress, inflammation, and DNA methylation in placental tissue. Exposure to PM2.5 during pregnancy also alters maternal thyroid hormone level and normal placental function, which can have a detrimental effect on pregnancy outcomes, such as preterm birth, low birth weight, and neurological abnormalities. This paper reviewed the effects of PM2.5 exposure during different trimesters on maternal and infant thyroid function, placental function, and pregnancy outcomes, aiming to provide more accurate protection of maternal and fetal health.

Strategical research on the refined regulations for regional air quality with climate synergy is essential for continuously improving the air quality, creating a coordinated multi-pollutants reduction path, and promoting sustainable development in China.This study analyzed the evolution characteristics of regional air pollution, the multi-pollutant interaction mechanisms, and the effectiveness of air pollution prevention and control strategies and technologies. Multi-perspective analysis and integrated research were conducted to clarify the non-linear relationship among multiple pollutants and formulate a technology system for refined regional regulation. The interaction between climate change and air pollution was explored as well. By summarizing the current technical routes for air pollution and control, the medium- and long-term air quality improvement strategies and roadmaps were proposed. Considering the current situation of air pollution in China, the regulation on atmospheric oxidation is the core for the coordinated control of PM2.5 and O3 pollution. Therefore, it is necessary to continuously promote emission reduction of primary pollutants and conduct refined and coordinated reduction of VOCs and NOx emissions considering specific climate and meteorological conditions. Additionally, coordinated emission reduction of multiple types of pollutants can be realized through adjustment of energy,transportation, industry, and landuse structures as well as low-carbon transformation, thereby synchronously reducing the PM2.5 and O3 concentrations.

Abstract Background: Dietary fish-oil supplementation might attenuate the associations between fine particulate matter (PM2.5) and subclinical biomarkers. However, the molecular mechanisms remain to be elucidated. This study aimed to explore the molecular mechanisms of fish-oil supplementation against the PM2.5-induced health effects. Methods: We conducted a randomized, double-blinded, and placebo-controlled trial among healthy college students in Shanghai, China, from September 2017 to January 2018. A total of 70 participants from the Fenglin campus of Fudan University were included. We randomly assigned participants to either supplementation of 2.5-gram fish oil (n = 35) or sunflower-seed oil (placebo) (n = 35) per day and conducted four rounds of health measurements in the last two months of the trial. As a post hoc exploratory study, the present untargeted metabolomics analysis used remaining blood samples collected in the previous trial and applied a Metabolome-Wide Association Study framework to compare the effects of PM2.5 on the metabolic profile between the sunflower-seed oil and fish oil groups. Results: A total of 65 participants completed the trial (34 of the fish oil group and 31 of the sunflower-seed oil group). On average, ambient PM2.5 concentration on the day of health measurements was 34.9 µg/m3 in the sunflower-seed oil group and 34.5 µg/m3 in the fish oil group, respectively. A total of 3833 metabolites were significantly associated with PM2.5 in the sunflower-seed oil group and 1757 in the fish oil group. Of these, 1752 metabolites showed significant between-group differences. The identified differential metabolites included arachidonic acid derivatives, omega-3 fatty acids, omega-6 fatty acids, and omega-9 fatty acids that were related to unsaturated fatty acid metabolism, which plays a role in the inflammatory responses. Conclusion: This trial suggests fish-oil supplementation could mitigate the PM2.5-induced inflammatory responses via modulating fatty acid metabolism, providing biological plausibility for the health benefits of fish-oil supplementation against PM2.5 exposure. Trial registration: This study is registered at ClinicalTrails.gov (NCT03255187).

Early life represents the most rapid and foundational period of brain development and a time of vulnerability to environmental insults. Evidence indicates that greater exposure to ubiquitous toxicants like fine particulate matter (PM 2.5), manganese, and many phthalates is associated with altered developmental, physical health, and mental health trajectories across the lifespan. Whereas animal models offer evidence of their mechanistic effects on neurological development, there is little research that evaluates how these environmental toxicants are associated with human neurodevelopment using neuroimaging measures in infant and pediatric populations. This review provides an overview of 3 environmental toxicants of interest in neurodevelopment that are prevalent worldwide in the air, soil, food, water, and/or products of everyday life: fine particulate matter (PM 2.5), manganese, and phthalates. We summarize mechanistic evidence from animal models for their roles in neurodevelopment, highlight prior research that has examined these toxicants with pediatric developmental and psychiatric outcomes, and provide a narrative review of the limited number of studies that have examined these toxicants using neuroimaging with pediatric populations. We conclude with a discussion of suggested directions that will move this field forward, including the incorporation of environmental toxicant assessment in large, longitudinal, multimodal neuroimaging studies; the use of multidimensional data analysis strategies; and the importance of studying the combined effects of environmental and psychosocial stressors and buffers on neurodevelopment. Collectively, these strategies will improve ecological validity and our understanding of how environmental toxicants affect long-term sequelae via alterations to brain structure and function. [ABSTRACT FROM AUTHOR]

Antibiotic resistance genes (ARGs) and antibiotic-resistant bacteria (ARB) in animal feces can be released into the atmosphere via aerosolization, posing a high health risk to farm workers. So far, little attention has been paid to the characterization of the aerosolization process. In this study, fecal and fine particulate matter (PM2.5) samples were collected from 20 animal farms involving swine, cattle, layers, and broilers, and the ARGs, ARB, and human pathogenic bacteria (HPB) were loaded in these two media. The results showed that approximately 70% of ARGs, 60% of ARBs, and 43% of HPBs were found to be preferential aerosolization. The bioaerosolization index (BI) of target 30 ARGs varied from 0.04 to 460.07, and the highest value was detected from tetW. The highest BI values of erythromycin- and tetracycline-resistant bacteria were for Kocuria (13119) and Staphylococcus (24746), respectively, and the distribution of BI in the two types of dominant ARB was similar. Regarding the bioaerosolization behavior of HPB, Clostridium saccharolyticum WM1 was the most easily aerosolized pathogen in swine and broiler farms, and Brucella abortus strain CNM 20040339 had the highest value in cattle and layer farms. Notably, the highest BI values for ARGs, ARB, and HPB were universally detected on chicken farms. Most ARGs, ARB, and HPB positively correlated with animal age, stocking density, and breeding area. Temperature and relative humidity have significant effects on the aerosolization behavior of targets, and the effects of these two parameters on the same target are usually opposite. The results of this study provide a basis for a better understanding of the contribution of animal feces to airborne ARGs and HPBs in farms, as well as for controlling the transport of the fecal microbiome to the environment through the aerosolization pathway. [ABSTRACT FROM AUTHOR]

With the rapid development of urbanization, ambient air pollution is becoming increasingly serious. Out of many pollutants, fine particulate matter (PM2.5) is the pollutant that affects the urban atmospheric environment to the greatest extent. Fine particulate matter (PM2.5) concentration prediction is of great significance to human health and environmental protection. This paper proposes a CNN-SSA-DBiLSTM-attention deep learning fusion model. This paper took the meteorological observation data and pollutant data from eight stations in Bijie from 1 January 2015 to 31 December 2022 as the sample data for training and testing. For the obtained data, the missing values and the data obtained from the correlation analysis performed were first processed. Secondly, a convolutional neural network (CNN) was used for the feature selection. DBILSTM was then used to establish a network model for the relationship between the input and actual output sequences, and an attention mechanism was added to enhance the impact of the relevant information. The number of units in the DBILSTM and the epoch of the whole network were optimized using the sparrow search algorithm (SSA), and the predicted value was the output after optimization. This paper predicts the concentration of PM2.5 in different time spans and seasons, and makes a comparison with the CNN-DBILSTM, BILSTM, and LSTM models. The results show that the CNN-SSA-DBiLSTM-attention model had the best prediction effect, and its accuracy improved with the increasing prediction time span. The coefficient of determination (R2) is stable at about 0.95. The results revealed that the proposed CNN-SSA-DBiLSTM-attention ensemble framework is a reliable and accurate method, and verifies the research results of this paper in regard to the prediction of PM2.5 concentration. This research has important implications for human health and environmental protection. The proposed method could inspire researchers to develop even more effective methods for atmospheric environment pollution modeling. [ABSTRACT FROM AUTHOR]

Air quality models can support pollution mitigation design by simulating policy scenarios and conducting source contribution analyses. The Intervention Model for Air Pollution (InMAP) is a powerful tool for equitable policy design as its variable resolution grid enables intra‐urban analysis, the scale of which most environmental justice inquiries are levied. However, InMAP underestimates particulate sulfate and overestimates particulate ammonium formation, errors that limit the model's relevance to city‐scale decision‐making. To reduce InMAP's biases and increase its relevancy for urban‐scale analysis, we calculate and apply scaling factors (SFs) based on observational data and advanced models. We consider both satellite‐derived speciated PM2.5 from Washington University and ground‐level monitor measurements from the U.S. Environmental Protection Agency, applied with different scaling methodologies. Relative to ground‐monitor data, the unscaled InMAP model fails to meet a normalized mean bias performance goal of <±10% for most of the PM2.5 components it simulates (pSO4: −48%, pNO3: 8%, pNH4: 69%), but with city‐specific SFs it achieves the goal benchmarks for every particulate species. Similarly, the normalized mean error performance goal of <35% is not met with the unscaled InMAP model (pSO4: 53%, pNO3: 52%, pNH4: 80%) but is met with the city‐scaling approach (15%–27%). The city‐specific scaling method also improves the R2 value from 0.11 to 0.59 (ranging across particulate species) to the range of 0.36–0.76. Scaling increases the percent pollution contribution of electric generating units (EGUs) (nationwide 4%) and non‐EGU point sources (nationwide 6%) and decreases the agriculture sector's contribution (nationwide −6%). Plain Language Summary: Air quality models can support the design of pollution reduction strategies by assessing sources of pollution and simulating policy scenarios. The Intervention Model for Air Pollution (InMAP) is an air quality model that can evaluate fine particulate matter (PM2.5) differences within cities, which makes it valuable as tool to assess equity of PM2.5 exposure. However, InMAP's simplified atmospheric chemistry equations results in errors that limit the model's relevance to city‐scale decision‐making. To reduce the model's biases and errors, we calculate and apply SFs based on observational data and advanced models, specifically ground‐level monitor measurements from the U.S. Environmental Protection Agency and a satellite‐derived data product. We find that applying SFs derived from satellite observations over cities or individual grid‐cells improves model performance. Scaling InMAP affects the source contribution analysis nationwide and for individual cities, specifically by increasing the contribution of power plants and industry and decreasing the contribution of the agriculture sector. Key Points: Applying scaling factors determined with satellite‐derived PM2.5 makes the Intervention Model for Air Pollution (InMAP) model suitable and relevant for urban‐scale analysisRelative to standard InMAP data, we find higher contributions of electric generating unit (EGU) and non‐EGU point sources to U.S. citiesRelative to standard InMAP data, we find lower contributions of the agricultural emissions to U.S. cities [ABSTRACT FROM AUTHOR]

In 2017, the Lancet Commission on Dementia Prevention, Intervention, and Care included air pollution in its list of potential risk factors for dementia; in 2018, the Lancet Commission on Pollution concluded that the evidence for a causal relationship between fine particulate matter (PM) and dementia is encouraging. However, few interventions exist to delay or prevent the onset of dementia. Air quality data are becoming increasingly available, and the science underlying the associated health effects is also evolving rapidly. Recent interest in this area has led to the publication of population-based cohort studies, but these studies have used different approaches to identify cases of dementia. The purpose of this article is to review recent evidence describing the association between exposure to air pollution and dementia with special emphasis on fine particulate matter of 2.5 microns or less. We also summarize here the proposed detailed mechanisms by which air pollutants reach the brain and activate the innate immune response. In addition, the article also provides a short overview of existing limitations in the treatment of dementia. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of air pollution on students' cognitive performance in a high-stakes exam: China's College English Test (CET). We match exams taken from 2013 to 2017 at 22 universities across China with hourly air pollution measures from the nearest monitoring stations. Identification leverages a student fixed effects model, which alleviates the concern of omitted variables, such as students' ability. Our estimates indicate a statistically significant negative impact of fine particulate matters (PM 2.5 ) exposure during exam windows on cognitive performance. By focusing on a single language exam, instead of comparing performance across different test subjects, we are able to paint a more accurate picture of the cognitive impact of air pollution. We highlight the importance of short-term air pollution exposure for high-stakes cognitive performance. Our results suggest that temporary defensive measures could be important in mitigating the negative consequences of air pollution. [ABSTRACT FROM AUTHOR]

Particulate matter less than 2.5 microns in diameter (PM2.5) is an air pollutant that has become a major environmental concern for governments around the world. Management and control require air quality monitoring and prediction. However, previous studies did not fully utilize the spectral information in multispectral satellite images and land use data in geographic datasets. To alleviate these problems, this study proposes the extraction of land use information not only from geographic inventory but also from satellite images with a machine learning-based classification. In this manner, near up-to-date land use data and spectral information from satellite images can be utilized, and the integration of geographic and remote sensing datasets boosts the accuracy of PM2.5 concentration modeling. In the experiments, Landsat-8 imagery with a 30-m spatial resolution was used, and cloud-free image generation was performed prior to the land cover classification. The proposed method, which uses predictors from geographic and multispectral satellite datasets in modeling, was compared with an approach which utilizes geographic and remote sensing datasets, respectively. Quantitative assessments showed that the proposed method and the developed model, with a performance of RMSE = 3.06 µg/m3 and R2 = 0.85 comparatively outperform the models with a performance of RMSE = 3.14 µg/m3 and R2 = 0.68 for remote sensing datasets and a performance of RMSE = 3.47 µg/m3 and R2 = 0.79 for geographic datasets. [ABSTRACT FROM AUTHOR]

The coordinated promotion of pollution control and carbon reduction is intended to build a more beautiful China. Intelligent industrial technology plays an important role in the fight against climate change and in improving the ecological environment. Based on panel data from 30 provinces in China from 2006 to 2020, we used a two-way fixed effects model to evaluate the synergistic effects of industrial intelligent transformation on pollution control and carbon reduction and its mechanisms. The results showed that the introduction and installation of industrial robots by enterprises significantly reduced carbon emissions and the concentration of fine particles in the air, as well as having the synergistic effect of reducing pollution and carbon. This conclusion was still robust after using instrumental variable methods to perform endogenous tests. The study also showed that industrial intelligence reduced pollution and carbon through mechanisms that promoted green technological innovation and improved energy efficiency. The conclusions of this study could provide evidence for the use of digital technologies to promote environmental protection and achieve the goal of carbon neutrality, as well as play a significant role in the promotion of economic and societal green transformation. [ABSTRACT FROM AUTHOR]

Background and Objective: There have been significant effects of the current coronavirus-19 (COVID-19) infection outbreak on many facets of everyday life, particularly the environment. Despite the fact that a number of studies have already been published on the topic, an analysis of those studies' findings on COVID-19's effects on environmental pollution is still lacking. The goal of the research is to look into greenhouse gas emissions and air pollution in Bangladesh when COVID-19 is under rigorous lockdown. The specific drivers of the asymmetric relationship between air pollution and COVID-19 are being investigated. Methods: The nonlinear relationship between carbon dioxide (C O 2 ) emissions, fine particulate matter (P M 2. 5) , and COVID-19, as well as its precise components, are also being investigated. To examine the asymmetric link between COVID-19 factors on C O 2 emissions and P M 2. 5 , we employed the nonlinear autoregressive distributed lag (NARDL) model. Daily positive cases and daily confirmed death by COVID-19 are considered the factors of COVID-19, with lockdown as a dummy variable. Results: The bound test confirmed the existence of long-run and short-run relationships between variables. Bangladesh's strict lockdown, enforced in reaction to a surge of COVID-19 cases, reduced air pollution and dangerous gas emissions, mainly C O 2 , according to the dynamic multipliers graph. [ABSTRACT FROM AUTHOR]

Short-term or long-term exposure to fine particulate matter (PM2.5) is related to increased incidences of respiratory diseases. This study aimed to investigate the influences of omega-3 polyunsaturated fatty acids (ω-3 PUFAs) supplementation on oxidative stress, inflammation, lung metabolic profile, and gut microbiota in PM2.5-induced lung injury mice. Mice were divided into four groups (n = 15, per group): two unsupplemented groups, control group and PM2.5 group, and two supplemented groups with ω-3 PUFAs, ω-3 PUFAs group, and ω-3 PUFAs + PM2.5 group. Mice in the supplemented groups were placed on an ω-3 PUFAs-enriched diet (ω-3 PUFAs, 21 g/kg). During the 5th to 6th week of dietary supplementation, mice were exposed to PM2.5 by intra-tracheal instillation. ω-3 PUFAs ameliorate lung histopathological injury, reduce inflammatory responses and oxidative stress, affect lung metabolite profile, and modulate gut microbiota in PM2.5-induced lung injury mice. Thus, supplementary ω-3 PUFAs showed effectiveness in attenuation of PM2.5-induced lung injury, indicating that the interventions exhibited preventive and therapeutic potential. [ABSTRACT FROM AUTHOR]

Long-term exposure to fine particulate matter (PM2.5) may increase the risk of neonatal encephalopathy due to birth asphyxia and trauma. However, little is known about the trends of PM2.5-related neonatal encephalopathy burden under different levels of social and economic development. We studied the burden of PM2.5-related neonatal encephalopathy due to birth asphyxia and trauma measured by the age-standardized mortality rate (ASMR) and the age-standardized disability-adjusted life years rate (ASDR), and its trends with the socio-demographic index (SDI) in 192 countries and regions from 1990 to 2019. This is a retrospective study using the Global Burden of Disease Study 2019 (GBD2019) database. The age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years rate (ASDR) are used to measure the burden of PM2.5-related neonatal encephalopathy in different countries and regions. The mortality rate (per 100 thousand) is used to evaluate the differences of PM2.5-related neonatal encephalopathy burden in sex and age. The annual percentage changes (APCs) and the average annual percentage changes (AAPCs) are used to reflect the trends of PM2.5-related neonatal encephalopathy burden over years (1990–2019) and are calculated using a Joinpoint model. The relationship of the socio-demographic index with the ASMR and ASDR is calculated using Gaussian process regression. In summary, the global burden of PM2.5-related neonatal encephalopathy increased since 1990, especially in boys, early neonates, and regions with low-middle SDI. Globally, the ASMR and ASDR of PM2.5-related neonatal encephalopathy burden in 2019 were 0.59 (95% CI: 0.40, 0.83) per 100,000 people and 52.59 (95% CI: 35.33, 73.67) per 100,000 people, respectively. From 1990 to 2019, the ASMR and ASDR of PM2.5-related neonatal encephalopathy increased by 44.39% and 44.19%, respectively. The global average annual percentage changes of ASMR and ASDR were 1.3 (95% CI: 1.0, 1.6). The relationship between the socio-demographic index and the burden of PM2.5-related neonatal encephalopathy presented negative correlation when the socio-demographic index was more than 0.60. Middle, high-middle, and high SDI regions had decreasing trends of PM2.5-related neonatal encephalopathy, of which the AAPCs for both ASMR and ASDR ranged from − 0.3 to − 3.1. Besides improving the progress in national policy and the coverage rate of maternal and neonatal health care and facility-based delivery, air pollution control may also be a better way for countries with large and increasing amounts of exposure to PM2.5 pollution to reduce neonatal encephalopathy. And our results also suggest that low and low-middle SDI countries should appropriately pay more attention to early newborns and boys. [ABSTRACT FROM AUTHOR]

Background: Tic disorders are common neurodevelopmental disorders during childhood. Whether prenatal and postnatal exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) plays a role in the development of tic disorders remains unexplored. Objectives: To investigate the association of exposure between PM2.5 during the pregnancy and infancy periods and the risk of tic disorders. Methods: This birth cohort study recruited singleton live births at term gestations in central Taiwan from the Taiwan Maternal and Child Health Database between 2004 and 2012 and followed up to the end of 2017. New cases of tic disorders were defined using the ICD‐9‐CM (307.2) and ICD‐10‐CM (F95), which include all tic spectrum disorders. We assigned daily PM2.5 concentrations derived from a satellite‐based model to individuals based on maternal residential addresses at delivery. We fit Cox proportional hazard model and distributed lag non‐linear model to estimate the associations between PM2.5 and tic disorders, with hazard ratio (HR) with 95% confidence interval (CI) as the effect measure. Results: Of the 309,376 singleton live births at term gestations, we identified 5902 (1.9%) tic disorder cases. The HR of tic disorders was positively associated with a 10 μg/m3 increase in PM2.5: during pregnancy HR 1.09, 95% CI 1.04, 1.15 and during infancy HR 1.12, 95% CI 1.06, 1.18. The vulnerable time window for infants with increased risk of tic disorders was 6–52 weeks after birth. We observed a nonlinear relationship between PM2.5 and the risk of tic disorders, with exposure to PM2.5 between 16 and 64 μg/m3 being associated with the risk of tic disorders. The association was restricted to Tourette's disorder group. Infant sex did not modify these associations. Conclusions: Infants delivered at term and exposed to PM2.5 are associated with an increased risk of tic disorders (6–52 weeks). Further studies are needed to confirm these associations. [ABSTRACT FROM AUTHOR]

Evidence from murine research supports that fine particulate matter (PM2.5) may stimulate the hypothalamic-pituitary-adrenal axis, leading to elevated circulating glucocorticoid levels. Epidemiologic research examining parallel associations document similar associations. We examined these associations among a diverse sample of pregnant individuals exposed to lower levels of ambient PM2.5. Participants included pregnant individuals enrolled in the PRogramming of Intergenerational Stress Mechanisms (PRISM) pre-birth cohort. Daily residential PM2.5 exposure was estimated using a satellite-based spatial-temporal hybrid model. Maternal 3rd trimester salivary cortisol levels were used to calculate several features of the diurnal cortisol rhythm. We used multivariable linear regression to examine PM2.5 during the pre-conception period and during each trimester in relation to cortisol awakening rise (CAR), slope, and area under the curve relative to ground (AUCG). The average PM2.5 exposure level across pregnancy was 8.13 µg/m3. PM2.5 in each exposure period was positively associated with AUCG, a measure of total cortisol output across the day. We also observed an inverse association between PM2.5 in the 3rd trimester and diurnal slope, indicating a steeper decline in cortisol throughout the day with increasing exposure. We did not detect strong associations between PM2.5 and slope for the other exposure periods or between PM2.5 and CAR for any exposure period. In this sample, PM2.5 exposure across the preconception and pregnancy periods was associated with increased cortisol output, even at levels below the U.S. National Ambient Air Quality Annual Standard for PM2.5 of 12.0 µg/m3. [ABSTRACT FROM AUTHOR]

In recent decades, air pollution in Ulaanbaatar has become a challenge regarding the health of the citizens of Ulaanbaatar, due to coal combustion in the ger area. Households burn fuel for cooking and to warm their houses in the morning and evening. This creates a difference between daytime and nighttime air pollution levels. The accurate mapping of air pollution and assessment of exposure to air pollution have thus become important study objects for researchers. The city center is where most air quality monitoring stations are located, but they are unable to monitor every residential region, particularly the ger area, which is where most particulate matter pollution originates. Due to this circumstance, it is difficult to construct an LUR model for the entire capital city's residential region. This study aims to map peak PM2.5 dispersion during the day using the Linear and Nonlinear Land Use Regression (LUR) model (Multi-Linear Regression Model (MLRM) and Generalized Additive Model (GAM)) for Ulaanbaatar, with monitoring station measurements and mobile device (DUST TRUK II) measurements. LUR models are frequently used to map small-scale spatial variations in element levels for various types of air pollution, based on measurements and geographical predictors. PM2.5 measurement data were collected and analyzed in the R statistical software and ArcGIS. The results showed the dispersion map MLRM R2 = 0.84, adjusted R2 = 0.83, RMSE = 53.25 µg/m3 and GAM R2 = 0.89, and adjusted R2 = 0.87, RMSE = 44 µg/m3. In order to validate the models, the LOOCV technique was run on both the MLRM and GAM. Their performance was also high, with LOOCV R2 = 0.83, RMSE = 55.6 µg/m3, MAE = 38.7 µg/m3, and GAM LOOCV R2 = 0.77, RMSE = 65.5 µg/m3, MAE = 47.7 µg/m3. From these results, the LUR model's performance is high, especially the GAM model, which works better than MRLM. [ABSTRACT FROM AUTHOR]

Fine particulate matter (PM2.5) from fireworks displays have been linked to serious health concerns, particularly in infants and children. Outdoor displays in large, recurring festivals such as state fairs thus may threaten local air quality, particularly given the proximity of fairgrounds to substantial, nearby residential populations. Here, we identify state fairs with known firework displays and assess their impact on air quality in nearby communities. We assessed the impact of three large, recurring festivals on PM2.5 levels in nearby communities. Overall, our multi-year analysis failed to identify measurable increases in PM2.5 concentrations during festival days at air quality monitoring sites within 4–10 km of the fairgrounds, even when data were filtered by wind direction. Results suggest that firework displays from such festivals are unlikely to violate PM2.5 air quality standards in communities near the fairgrounds. The results suggest that identifying a potential air pollution signal associated with fireworks is challenging, particularly in urban fairgrounds where air quality is impacted by multiple local and distant pollution sources. Local impacts may yet be identified in future studies if air quality is monitored closer to the fairgrounds and if the fireworks pyrotechnic content is known. [ABSTRACT FROM AUTHOR]

Abstract Few studies have explored the effects of fine particulate matter (PM2.5) and its constituents on the progression of cerebral blood flow velocity (BFV) and the potential modifying role of greenness. In this study, we investigated the association of PM2.5 and its constituents, including sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), organic matter (OM), and black carbon (BC), with the progression of BFV in the middle cerebral artery. Participants from the Beijing Health Management Cohort who underwent at least two transcranial Doppler sonography examinations during 2015–2020 were recruited. BFV change and BFV change rate were used to define the progression of cerebral BFV. Linear mixed effects models were employed to analyze the data, and the weighted quantile sum regression assessed the contribution of PM2.5 constituents. Additionally, greenness was examined as a modifier. Among the examined constituents, OM exhibited the strongest association with BFV progression. An interquartile range increase in PM2.5 and OM exposure concentrations was associated with a decrease of −16.519 cm/s (95% CI: −17.837, −15.201) and −15.403 cm/s (95% CI: −16.681, −14.126) in BFV change, and −10.369 cm/s/year (95% CI: −11.387, −9.352) and −9.615 cm/s/year (95% CI: −10.599, −8.632) in BFV change rate, respectively. Furthermore, stronger associations between PM2.5 and BFV progression were observed in individuals working in areas with lower greenness, those aged under 45 years, and females. In conclusion, reducing PM2.5 levels in the air, particularly the OM constituent, and enhancing greenness could potentially contribute to the protection of cerebrovascular health.

Background: Ultrafine particles, including black carbon (BC), can reach the systemic circulation and therefore may distribute to distant organs upon inhalation. The kidneys may be particularly vulnerable to the adverse effects of BC exposure due to their filtration function. Objectives: We hypothesized that BC particles reach the kidneys via the systemic circulation, where the particles may reside in structural components of kidney tissue and impair kidney function. Methods: In kidney biopsies from 25 transplant patients, we visualized BC particles using white light generation under femtosecond-pulsed illumination. The presence of urinary kidney injury molecule-1 (KIM-1) and cystatin c (CysC) were evaluated with ELISA. We assessed the association between internal and external exposure matrices and urinary biomarkers using Pearson correlation and linear regression models. Results: BC particles could be identified in all biopsy samples with a geometric mean (5th, 95th percentile) of 1.80 × 103 (3.65 × 102, 7.50 × 103) particles/mm3 kidney tissue, predominantly observed in the interstitium (100 %) and tubules (80 %), followed by the blood vessels and capillaries (40 %), and the glomerulus (24 %). Independent from covariates and potential confounders, we found that each 10 % higher tissue BC load resulted in 8.24 % (p = 0.03) higher urinary KIM-1. In addition, residential proximity to a major road was inversely associated with urinary CysC (+10 % distance: −4.68 %; p = 0.01) and KIM-1 (+10 % distance: −3.99 %; p

There have been few studies in non-western countries on the relationship between low levels of daily fine particulate matter (PM2.5) exposure and morbidity or mortality, and the impact of PM2.5 concentrations below 15 μg/m3, which is the latest World Health Organization Air Quality Guideline (WHO AQG) value for the 24-h mean, is not yet clear. We assessed the associations between low-level PM2.5 exposure and cardiorespiratory admissions in Japan. We collected the daily hospital admission count data, air pollutant data, and meteorological condition data recorded from April 2016 to March 2019 in 139 Japanese cities. City-specific estimates were obtained from conditional logistic regression models in a time-stratified case-crossover design and pooled by random-effect models. We estimated that every 10-μg/m3 increase in the concurrent-day PM2.5 concentration was related to a 0.52% increase in cardiovascular admissions (95% CI: 0.13–0.92%) and a 1.74% increase in respiratory admissions (95% CI: 1.41–2.07%). These values were nearly the same when the datasets were filtered to contain only daily PM2.5 concentrations

Maternal exposure to ambient fine particulate matter (PM2.5) during pregnancy has been associated with impaired neurobehavioral development in children. However, the specific mechanism remains unclear. Brain derived neurotrophic factor (BDNF) is an important growth factor in the nervous system. We evaluated the associations of maternal PM2.5 exposures with fetal BDNF in the umbilical cord blood in a prospective cohort study. A total of 711 eligible mother-infant pairs from the Shanghai Birth Cohort were included in the current study. Daily maternal exposures to ambient PM2.5 were assessed with a gap-filling approach at 1 * 1 km2 resolution based on self-reported home addresses. The concentrations of BDNF in the cord blood were measured by ELISA. A linear regression model was applied to evaluate the association of maternal ambient PM2.5 exposure with fetal BDNF level at birth. The median concentration of BDNF was 13,403 pg/ml. Vaginal deliveries and female infants had higher BDNF levels than cesarean deliveries and male infants. One natural log (ln) unit increase in maternal PM2.5 exposure during the second trimester was significantly associated with − 0.20 (95% CI: −0.36, −0.05) ln-unit decrease in BDNF level in all births. These effects were stronger and more significant in vaginal deliveries and in male infants. Our study suggests that BDNF in the cord blood may serve as a potential biomarker in assessing the neurodevelopmental effects of maternal PM2.5 exposure.

While China’s clean air actions implemented since 2013 have been effective in mitigating PM _2.5 air pollution, the large emission reductions during the COVID-19 lockdown period in early 2020 did not similarly alleviate PM _2.5 pollution in North China, reflecting a distinct nonlinear chemical response of PM _2.5 formation to emission changes. Here we apply emission-concentration relationships for PM _2.5 diagnosed using the adjoint approach to quantitatively assess how chemical nonlinearity affects PM _2.5 over Beijing in February 2020 in response to two emission reduction scenarios: the COVID-19 lockdown and 2013–2017 emission controls. We find that, in the absence of chemical nonlinearity, the COVID-19 lockdown would decrease PM _2.5 in Beijing by 17.9 μ g m ^–3 , and the 2013–2017 emission controls resulted in a larger decrease of 54.2 μ g m ^–3 because of greater reductions of SO _2 and primary aerosol emissions. Chemical nonlinearity offset the decrease for Beijing PM _2.5 by 3.4 μ g m ^–3 during the lockdown due to enhanced sensitivity of aerosol nitrate to NO _x emissions, but enhanced the efficiency of 2013–2017 emission controls by 11.9 μ g m ^–3 due to the weakened heterogeneous reaction of sulfate. Such nonlinear chemical effects are important to estimate and consider when designing or assessing air pollution control strategies.

Racial-ethnic minority populations in the US are disproportionately exposed to airborne fine particulate matter (PM _2.5 ), but few national studies have focused individually on the sources that contribute to these disparities. We address this gap by conducting a comprehensive analysis of PM _2.5 exposure disparities by race-ethnicity in the US, focusing on three source-categories: mobile-sources, cooking, and all other sources combined. Our approach is based on high-resolution, national land-use regression estimates of source-resolved PM _2.5 components, derived from high-resolution aerosol mass spectrometer measurements. We find that each of these sources contributes approximately one-third of the overall PM _2.5 exposure disparities by race-ethnicity. While the importance of mobile-source tailpipe emissions is well recognized, our study underscores the significance of cooking emissions in creating PM _2.5 exposure disparities. This finding represents a potentially significant opportunity to reduce these disparities, as cooking emissions are currently largely unregulated. It has important implications for policymakers and public health advocates aiming to address the persistent issue of racial-ethnic disparities in air pollution.

Prior studies suggest brick workers in Nepal may be chronically exposed to hazardous levels of fine particulate matter (PM2.5) from ambient, occupational, and household sources. However, findings from these studies were based on stationary monitoring data, and thus may not reflect a worker’s individual exposures. In this study, we used RTI International’s MicroPEMs to collect 24 h PM2.5 personal breathing zone (PBZ) samples among brick workers (n = 48) to estimate daily exposures from ambient, occupational, and household air pollution sources. Participants were sampled from five job categories at one kiln. The geometric mean (GM) PM2.5 exposure across all participants was 116 µg/m3 (95% confidence interval [CI]: 94.03, 143.42). Job category was significantly (p < 0.001) associated with PBZ PM2.5 concentrations. There were significant pairwise differences in geometric mean (GM) PBZ PM2.5 concentrations among workers in administration (GM: 47.92, 95% CI: 29.81, 77.03 µg/m3) vs. firemen (GM: 163.46, 95 CI: 108.36, 246.58 µg/m3, p = 0.003), administration vs. green brick hand molder (GM: 163.35, 95% CI: 122.15, 218.46 µg/m3, p < 0.001), administration vs. top loader (GM: 158.94, 95% CI: 102.42, 246.66 µg/m3, p = 0.005), firemen vs. green brick machine molder (GM: 73.18, 95% CI: 51.54, 103.90 µg/m3, p = 0.03), and green brick hand molder vs. green brick machine molder (p = 0.008). Temporal exposure trends suggested workers had chronic exposure to hazardous levels of PM2.5 with little to no recovery period during non-working hours. Multi-faceted interventions should focus on the control of ambient and household air pollution and tailored job-specific exposure controls.

Variations in meteorology associated with climate change can impact fine particulate matter (PM2.5) pollution by affecting natural emissions, atmospheric chemistry, and pollutant transport. However, substantial discrepancies exist among model-based projections of PM2.5 impacts driven by anthropogenic climate change. Natural variability can significantly contribute to the uncertainty in these estimates. Using a large ensemble of climate and atmospheric chemistry simulations, we evaluate the influence of natural variability on projections of climate change impacts on PM2.5 pollution in the United States. We find that natural variability in simulated PM2.5 can be comparable or larger than reported estimates of anthropogenic-induced climate impacts. Relative to mean concentrations, the variability in projected PM2.5 climate impacts can also exceed that of ozone impacts. Based on our projections, we recommend that analyses aiming to isolate the effect climate change on PM2.5 use 10 years or more of modeling to capture the internal variability in air quality and increase confidence that the anthropogenic-forced effect is differentiated from the noise introduced by natural variability. Projections at a regional scale or under greenhouse gas mitigation scenarios can require additional modeling to attribute impacts to climate change. Adequately considering natural variability can be an important step toward explaining the inconsistencies in estimates of climate-induced impacts on PM2.5. Improved treatment of natural variability through extended modeling lengths or initial condition ensembles can reduce uncertainty in air quality projections and improve assessments of climate policy risks and benefits.

Fine particulate matter (PM2.5) air pollution exposure is the largest environmental health risk factor in the United States. Here, we link PM2.5 exposure to the human activities responsible for PM2.5 pollution. We use these results to explore "pollution inequity": the difference between the environmental health damage caused by a racial-ethnic group and the damage that group experiences. We show that, in the United States, PM2.5 exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities. On average, non-Hispanic whites experience a "pollution advantage": They experience ∼17% less air pollution exposure than is caused by their consumption. Blacks and Hispanics on average bear a "pollution burden" of 56% and 63% excess exposure, respectively, relative to the exposure caused by their consumption. The total disparity is caused as much by how much people consume as by how much pollution they breathe. Differences in the types of goods and services consumed by each group are less important. PM2.5 exposures declined ∼50% during 2002-2015 for all three racial-ethnic groups, but pollution inequity has remained high.

Air pollution has always been an important factor threatening population health, and with the acceleration of urbanization in China, the adverse health effects associated with air pollution is becoming more and more serious. Numerous scientific studies have shown that chemical components of fine particulate matter are closely related to human health damage. This paper elaborated reported human health outcomes of PM2.5 chemical components, including fatality, morbidity, reproduction & development, and physiological indexes or biomarkers, reviewed the research progress of PM2.5 chemical constituents on human health in China, and summarized the deficiencies of current research, aiming to provide useful clues for future relevant studies.

Abstract Objectives To estimate associations between fine particulate matter (PM2.5) and ozone and the onset of systemic autoimmune rheumatic diseases (SARDs). Methods An open cohort of over 6 million adults was constructed from provincial physician billing and hospitalization records between 2000 and 2013. We defined incident SARD cases (SLE, Sjogren’s syndrome, scleroderma, polymyositis, dermatomyositis, polyarteritis nodosa and related conditions, polymyalgia rheumatic, other necrotizing vasculopathies, and undifferentiated connective tissue disease) based on at least two relevant billing diagnostic codes (within 2 years, with at least 1 billing from a rheumatologist), or at least one relevant hospitalization diagnostic code. Estimated PM2.5 and ozone concentrations (derived from remote sensing and/or chemical transport models) were assigned to subjects based on residential postal codes, updated throughout follow-up. Cox proportional hazards models with annual exposure levels were used to calculate hazard ratios (HRs) for SARDs incidence, adjusting for sex, age, urban-versus-rural residence, and socioeconomic status. Results The adjusted HR for SARDS related to one interquartile range increase in PM2.5 (3.97 µg/m3) was 1.12 (95% confidence interval 1.08–1.15), but there was no clear association with ozone. Indirectly controlling for smoking did not alter the findings. Conclusions We found associations between SARDs incidence and PM2.5, but no relationships with ozone. Additional studies are needed to better understand interplays between the many constituents of air pollution and rheumatic diseases.