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Recent studies have linked the cardiovascular events with the exposure to ambient fine particulate matter (PM2.5); however, the impact of PM2.5 chemical components on acute myocardial infarction (AMI) case fatality remains poorly understood. To address this gap, we included 178,340 hospitalised patients with AMI utilising the inpatient discharge database from Sichuan, Shanxi, Guangxi, and Guangdong, China spanning 2014–2019. We evaluated exposure to PM2.5 and its components (black carbon (BC), organic matter (OM), sulphate (SO42−), nitrate (NO3−), and ammonium (NH4+)) using bilinear interpolation based on the patient’s residential address. We used mixed-effects logistic regression models to investigate the associations of PM2.5 and its five components with in-hospital AMI case fatality. Per interquartile range (IQR) increment in short-term exposure (7-day average) to overall PM2.5 (odds ratio (OR): 1.086, 95 % confidence interval (CI): 1.045–1.128), SO42−(1.063, 1.024–1.104), BC (1.055, 1.023–1.089), OM (1.052, 1.019–1.086, and NO3− (1.045, 1.003–1.089) were significantly associated with high risk of in-hospital AMI case fatality. The ORs per IQR increment in long-term exposure (annual average) were 1.323 (95 % CI: 1.255–1.394) for PM2.5, followed by BC (1.271, 1.210–1.335), OM (1.243, 1.188–1.300), SO42− (1.212, 1.157–1.270), NO3− (1.116, 1.075–1.159), and NH4+ (1.068, 1.031–1.106). Our study suggests that PM2.5 chemical components might be important risk factors for in-hospital AMI case fatality, highlighting the importance of targeted reduction of PM2.5 emissions, particularly BC, OM, and SO42−.

This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM2.5) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up (N = 4559) were identified by ICD codes. Prenatal exposures to PM2.5, elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO3-), and sulfate (SO42-) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM2.5, EC/BC, OM, and SO42- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO3-. EC/BC, OM, and SO42- were generally robust to adjustment for other components and for PM2.5. EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO42- in the third trimester. Future PM2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.

Background: PM2.5 and its chemical components increase health risks and are associated with depression and gut microbiota. However, there is still limited evidence on whether gut microbiota and short-chain fatty acids (SCFAs) mediate the association between PM2.5, PM2.5 chemical components, and antenatal depression. The purpose of this study was to investigate the mediating role of maternal gut microbiota in correlations between short-term exposure to PM2.5, short-term exposure to PM2.5 chemical components, and antenatal depression. Methods: Demographic information and stool samples were collected from 75 pregnant women in their third trimester. Their exposure to PM2.5 and PM2.5 chemical components was measured. Participants were divided into the non-antenatal depression group or the antenatal depression group according to the cut-off of 10 points on the Edinburgh Postnatal Depression Scale (EPDS). The gut microbiota were analyzed using the 16 S rRNA-V3/V4 gene sequence, and the concentration of PM2.5 and its chemical components was calculated using the Tracking Air Pollution in China (TAP) database. Gas chromatography–mass spectrometry was used to analyze SCFAs in stool samples. In order to assess the mediating effects of gut microbiota and SCFAs, mediation models were utilized. Results: There were significant differences between gut microbial composition and SCFAs concentrations between the non-antenatal depression group and the antenatal depression group. PM2.5 and its chemical components were positively associated with EPDS scores and negatively associated with genera Enterococcus and Enterobacter. Genera Candidatus_Soleaferrea (β = −7.21, 95%CI −11.00 to −3.43, q = 0.01) and Enterococcus (β = −2.37, 95%CI −3.87 to −0.87, q = 0.02) were negatively associated with EPDS scores, indicating their potential protective effects against antenatal depression. There was no significant association between SCFAs and EPDS scores. The mediating role of Enterococcus between different lagged periods of PM2.5, PM2.5 chemical component exposure, and antenatal depression was revealed. For instance, Enterococcus explained 29.23% (95%CI 2.16–87.13%, p = 0.04) of associations between PM2.5 exposure level at the day of sampling (lag 0) and EPDS scores. Conclusion: Our study highlights that Enterococcus may mediate the associations between PM2.5, PM2.5 chemical components, and antenatal depression. The mediating mechanism through which the gut microbiota influences PM2.5-induced depression in pregnant women still needs to be further studied.

Background: The association between specific chemical components of PM2.5 and depression remains largely unknown. Methods: We conducted a time-stratified case-crossover analysis with a distributed lag nonlinear model (DLNM) to evaluate the relationship of PM2.5 and its chemical components, including black carbon (BC), organic matter (OM), sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+), with the depression incidence. Daily depression outpatients were enrolled from Huizhou, Shenzhen, and Zhaoqing. Results: Among 247,281 outpatients, we found the strongest cumulative effects of PM2.5 and its chemical components with the odd ratios (ORs) of 1.607 (95% CI: 1.321, 1.956) and 1.417 (95% CI: 1.245, 1.612) at the 50th percentile of PM2.5 and OM at lag 21, respectively. Furthermore, the ORs with SO42− and NH4+ at the 75th percentile on the same lag day were 1.418 (95% CI: 1.247, 1.613) and 1.025 (95% CI: 1.009, 1.140). Relatively stronger associations were observed among females and the elderly. Conclusions: Our study suggests that PM2.5 and its chemical components might be important risk factors for depression. Reducing PM2.5 emissions, with a particular focus on the major sources of SO42− and OM, might potentially alleviate the burden of depression in South China. [ABSTRACT FROM AUTHOR]

Background: Autism Spectrum Disorder (ASD) risk is highly heritable, with potential additional non-genetic factors, such as prenatal exposure to ambient particulate matter with aerodynamic diameter

Background: The association between specific chemical components of PM2.5 and depression remains largely unknown. Methods: We conducted a time-stratified case-crossover analysis with a distributed lag nonlinear model (DLNM) to evaluate the relationship of PM2.5 and its chemical components, including black carbon (BC), organic matter (OM), sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+), with the depression incidence. Daily depression outpatients were enrolled from Huizhou, Shenzhen, and Zhaoqing. Results: Among 247,281 outpatients, we found the strongest cumulative effects of PM2.5 and its chemical components with the odd ratios (ORs) of 1.607 (95% CI: 1.321, 1.956) and 1.417 (95% CI: 1.245, 1.612) at the 50th percentile of PM2.5 and OM at lag 21, respectively. Furthermore, the ORs with SO42− and NH4+ at the 75th percentile on the same lag day were 1.418 (95% CI: 1.247, 1.613) and 1.025 (95% CI: 1.009, 1.140). Relatively stronger associations were observed among females and the elderly. Conclusions: Our study suggests that PM2.5 and its chemical components might be important risk factors for depression. Reducing PM2.5 emissions, with a particular focus on the major sources of SO42− and OM, might potentially alleviate the burden of depression in South China.

Recent studies have linked the cardiovascular events with the exposure to ambient fine particulate matter (PM 2.5); however, the impact of PM 2.5 chemical components on acute myocardial infarction (AMI) case fatality remains poorly understood. To address this gap, we included 178,340 hospitalised patients with AMI utilising the inpatient discharge database from Sichuan, Shanxi, Guangxi, and Guangdong, China spanning 2014–2019. We evaluated exposure to PM 2.5 and its components (black carbon (BC), organic matter (OM), sulphate (SO 4 2 −), nitrate (NO 3 −), and ammonium (NH 4 +)) using bilinear interpolation based on the patient's residential address. We used mixed-effects logistic regression models to investigate the associations of PM 2.5 and its five components with in-hospital AMI case fatality. Per interquartile range (IQR) increment in short-term exposure (7-day average) to overall PM 2.5 (odds ratio (OR): 1.086, 95 % confidence interval (CI): 1.045–1.128), SO 4 2 − (1.063, 1.024–1.104), BC (1.055, 1.023–1.089), OM (1.052, 1.019–1.086, and NO 3 − (1.045, 1.003–1.089) were significantly associated with high risk of in-hospital AMI case fatality. The ORs per IQR increment in long-term exposure (annual average) were 1.323 (95 % CI: 1.255–1.394) for PM 2.5 , followed by BC (1.271, 1.210–1.335), OM (1.243, 1.188–1.300), SO 4 2 − (1.212, 1.157–1.270), NO 3 − (1.116, 1.075–1.159), and NH 4 + (1.068, 1.031–1.106). Our study suggests that PM 2.5 chemical components might be important risk factors for in-hospital AMI case fatality, highlighting the importance of targeted reduction of PM 2.5 emissions, particularly BC, OM, and SO 4 2 −. • First study to assess the associations of PM 2.5 components with AMI case fatality. • Exposure to PM 2.5 components increases the risk of in-hospital AMI case fatality. • Long-term exposure showed stronger associations than short-term exposure. • Reducing PM 2.5 components from combustion sources (BC, OM, and SO 4 2 −) is crucial. [ABSTRACT FROM AUTHOR]

Unhealthy levels of fine particulate matter (PM2.5) from the local burning of solid fuels, and from regional transport of pollutants, remain a major public health problem in the Himalayan foothill villages in Nepal. Teahouses (i.e., mountain lodges) along popular hiking trails in the lower Himalayas commonly use wood as the primary energy source for heating; however, little is known about teahouse air quality. The purpose of this study was to characterize the levels and chemical constituents of indoor and ambient PM2.5 at three villages along the Poon Hill circuit trek in the Annapurna Conservation Area in Nepal. A convenience sample of five PM2.5 measurements was collected with portable MicroPEM V.3.2A exposure monitors. Filters were analyzed for black and brown carbon using integrating sphere optical transmittance and 33 elemental constituents using energy-dispersive X-ray fluorescence. Median indoor PM2.5 over the sampling period was 41.3 µg/m3, whereas median ambient PM2.5 over the sampling period was 34.7 µg/m3. Chemical species associated with wood smoke, such as potassium (GM = 0.88 µg/m3), predominated. High indoor and ambient PM2.5 levels may pose a significant occupational health risk to teahouse workers, who may experience chronic exposures during trekking seasons. Our findings warrant additional research to characterize teahouse air pollution exposures more fully and to evaluate intervention measures. [ABSTRACT FROM AUTHOR]

PM 2.5 and its chemical components increase health risks and are associated with depression and gut microbiota. However, there is still limited evidence on whether gut microbiota and short-chain fatty acids (SCFAs) mediate the association between PM 2.5 , PM 2.5 chemical components, and antenatal depression. The purpose of this study was to investigate the mediating role of maternal gut microbiota in correlations between short-term exposure to PM 2.5 , short-term exposure to PM 2.5 chemical components, and antenatal depression. Demographic information and stool samples were collected from 75 pregnant women in their third trimester. Their exposure to PM 2.5 and PM 2.5 chemical components was measured. Participants were divided into the non-antenatal depression group or the antenatal depression group according to the cut-off of 10 points on the Edinburgh Postnatal Depression Scale (EPDS). The gut microbiota were analyzed using the 16 S rRNA-V3/V4 gene sequence, and the concentration of PM 2.5 and its chemical components was calculated using the Tracking Air Pollution in China (TAP) database. Gas chromatography–mass spectrometry was used to analyze SCFAs in stool samples. In order to assess the mediating effects of gut microbiota and SCFAs, mediation models were utilized. There were significant differences between gut microbial composition and SCFAs concentrations between the non-antenatal depression group and the antenatal depression group. PM 2.5 and its chemical components were positively associated with EPDS scores and negatively associated with genera Enterococcus and Enterobacter. Genera Candidatus_Soleaferrea (β = −7.21, 95%CI −11.00 to −3.43, q = 0.01) and Enterococcus (β = −2.37, 95%CI −3.87 to −0.87, q = 0.02) were negatively associated with EPDS scores, indicating their potential protective effects against antenatal depression. There was no significant association between SCFAs and EPDS scores. The mediating role of Enterococcus between different lagged periods of PM 2.5 , PM 2.5 chemical component exposure, and antenatal depression was revealed. For instance, Enterococcus explained 29.23% (95%CI 2.16–87.13%, p = 0.04) of associations between PM 2.5 exposure level at the day of sampling (lag 0) and EPDS scores. Our study highlights that Enterococcus may mediate the associations between PM 2.5 , PM 2.5 chemical components, and antenatal depression. The mediating mechanism through which the gut microbiota influences PM 2.5 -induced depression in pregnant women still needs to be further studied. [Display omitted] • PM 2.5 , PM 2.5 chemical components were associated with antenatal depression. • Gut microbiota was associated with PM 2.5 , PM 2.5 chemical components. • Gut microbiota was associated with antenatal depression. • Enterococcus mediated linkage between PM 2.5 and antenatal depression. • Enterococcus mediated linkage between PM 2.5 components and antenatal depression. [ABSTRACT FROM AUTHOR]

[Display omitted] • Prenatal conditions of maternal immune activation (MIA) was associated with ASD. • Exposure to PM 2.5 and its major components during pregnancy were associated with ASD. • Prenatal MIA conditions unexpectedly did not augment effects of particles on ASD. Autism Spectrum Disorder (ASD) risk is highly heritable, with potential additional non-genetic factors, such as prenatal exposure to ambient particulate matter with aerodynamic diameter < 2.5 µm (PM 2.5) and maternal immune activation (MIA) conditions. Because these exposures may share common biological effect pathways, we hypothesized that synergistic associations of prenatal air pollution and MIA-related conditions would increase ASD risk in children. This study examined interactions between MIA-related conditions and prenatal PM 2.5 or major PM 2.5 components on ASD risk. In a population-based pregnancy cohort of children born between 2001 and 2014 in Southern California, 318,751 mother–child pairs were followed through electronic medical records (EMR); 4,559 children were diagnosed with ASD before age 5. Four broad categories of MIA-related conditions were classified, including infection, hypertension, maternal asthma, and autoimmune conditions. Average exposures to PM 2.5 and four PM 2.5 components, black carbon (BC), organic matter (OM), nitrate (NO 3 –), and sulfate (SO 4 2-), were estimated at maternal residential addresses during pregnancy. We estimated the ASD risk associated with MIA-related conditions, air pollution, and their interactions, using Cox regression models to adjust for covariates. ASD risk was associated with MIA-related conditions [infection (hazard ratio 1.11; 95% confidence interval 1.05–1.18), hypertension (1.30; 1.19–1.42), maternal asthma (1.22; 1.08–1.38), autoimmune disease (1.19; 1.09–1.30)], with higher pregnancy PM 2.5 [1.07; 1.03–1.12 per interquartile (3.73 μg/m3) increase] and with all four PM 2.5 components. However, there were no interactions of each category of MIA-related conditions with PM 2.5 or its components on either multiplicative or additive scales. MIA-related conditions and pregnancy PM 2.5 were independently associations with ASD risk. There were no statistically significant interactions of MIA conditions and prenatal PM 2.5 exposure with ASD risk. [ABSTRACT FROM AUTHOR]

Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellitederived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satelliteestimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM2.5 data with a spatial resolution of 0.1° × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of -35.9%, NME of 48.2%, ARB_50% of 53.68% for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO4 2- and NH4 + concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO42-: -0.61%; NH4 +: -0.21%), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004-2007 and 2008-2011, followed by a negative trend over the period 2012-2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as follows: winter > summer > autumn > spring. High concentrations of these species were concentrated in the NCP and SCB, originating from coal-fired power plants and agricultural activities, respectively. Efforts to reduce sulfur dioxide (SO2) emissions have yielded remarkable results since the government has adopted stricter control measures in recent years. Moreover, ammonia emissions should be controlled while reducing the concentration of sulfur, nitrogen and particulate matter. This study provides an assessment of the population's exposure to certain chemical components. [ABSTRACT FROM AUTHOR]

Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM2.5 data with a spatial resolution of 0.1 × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of -35.9%, NME of 48.2%, ARB_50% of 53.68% for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO42- and NH4+ concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO42-: -0.61%; NH4+: -0.21%), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004-2007 and 2008-2011, followed by a negative trend over the period 2012-2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as follows: winter > summer > autumn > spring. High concentrations of these species were concentrated in the NCP and SCB, originating from coal-fired power plants and agricultural activities, respectively. Efforts to reduce sulfur dioxide (SO2) emissions have yielded remarkable results since the government has adopted stricter control measures in recent years. Moreover, ammonia emissions should be controlled while reducing the concentration of sulfur, nitrogen and particulate matter. This study provides an assessment of the population's exposure to certain chemical components. [ABSTRACT FROM AUTHOR]

Epidemiologic and health impact studies have examined the chemical composition of ambient PM2.5 in China but have been constrained by the paucity of long-term ground measurements. Using the GEOS-Chem chemical transport model and satellite-derived PM2.5 data, sulfate and ammonium levels were estimated over China from 2004 to 2014. A comparison of the satellite-estimated dataset with model simulations based on ground measurements obtained from the literature indicated our results are more accurate. Using satellite-derived PM2.5 data with a spatial resolution of 0.1 × 0.1°, we further presented finer satellite-estimated sulfate and ammonium concentrations in anthropogenic polluted regions, including the NCP (the North China Plain), the SCB (the Sichuan Basin) and the PRD (the Pearl River Delta). Linear regression results obtained on a national scale yielded an r value of 0.62, NMB of −35.9%, NME of 48.2%, ARB_50% of 53.68% for sulfate and an r value of 0.63, slope of 0.67, and intercept of 5.14 for ammonium. In typical regions, the satellite-derived dataset was significantly robust. Based on the satellite-derived dataset, the spatial-temporal variation of 11-year annual average satellite-derived SO42− and NH4+ concentrations and time series of monthly average concentrations were also investigated. On a national scale, both exhibited a downward trend each year between 2004 and 2014 (SO42−: −0.61%; NH4+: −0.21%), large values were mainly concentrated in the NCP and SCB. For regions captured at a finer resolution, the inter-annual variation trends presented a positive trend over the periods 2004–2007 and 2008–2011, followed by a negative trend over the period 2012–2014, and sulfate concentrations varied appreciably. Moreover, the seasonal distributions of the 11-year satellite-derived dataset over China were presented. The distribution of both sulfate and ammonium concentrations exhibited seasonal characteristics, with the seasonal concentrations ranking as follows: winter > summer > autumn > spring. High concentrations of these species were concentrated in the NCP and SCB, originating from coal-fired power plants and agricultural activities, respectively. Efforts to reduce sulfur dioxide (SO2) emissions have yielded remarkable results since the government has adopted stricter control measures in recent years. Moreover, ammonia emissions should be controlled while reducing the concentration of sulfur, nitrogen and particulate matter. This study provides an assessment of the population’s exposure to certain chemical components.

This retrospective cohort study examined associations of autism spectrum disorder (ASD) with prenatal exposure to major fine particulate matter (PM 2.5 ) components estimated using two independent exposure models. The cohort included 318 750 mother-child pairs with singleton deliveries in Kaiser Permanente Southern California hospitals from 2001 to 2014 and followed until age five. ASD cases during follow-up ( N = 4559) were identified by ICD codes. Prenatal exposures to PM 2.5 , elemental (EC) and black carbon (BC), organic matter (OM), nitrate (NO 3 - ), and sulfate (SO 4 2- ) were constructed using (i) a source-oriented chemical transport model and (ii) a hybrid model. Exposures were assigned to each maternal address during the entire pregnancy, first, second, and third trimester. In single-pollutant models, ASD was associated with pregnancy-average PM 2.5 , EC/BC, OM, and SO 4 2- exposures from both exposure models, after adjustment for covariates. The direction of effect estimates was consistent for EC/BC and OM and least consistent for NO 3 - . EC/BC, OM, and SO 4 2- were generally robust to adjustment for other components and for PM 2.5 . EC/BC and OM effect estimates were generally larger and more consistent in the first and second trimester and SO 4 2- in the third trimester. Future PM 2.5 composition health effect studies might consider using multiple exposure models and a weight of evidence approach when interpreting effect estimates.