The present study primarily focuses on describing aerosol optical depth (AOD), its distribution pattern and seasonal variation, and modelling Particulate Matter Concentrations in Chennai. The frequency distribution of AOD and PM2.5 demonstrates that AOD can be used as a proxy for estimating PM2.5 in the study region as the occurrence of AOD almost resonates with that of PM2.5. The seasonal variation of AOD and PM2.5 revealed that during the winter (October–January) and summer (February–May) seasons, AOD reasonably followed the trend of PM2.5. However, during the monsoon period, AOD showed random variations. Different models like linear and non-linear regression models and machine learning models such as random forest (RF) have been developed for PM2.5 estimation. The model's performance in different stations and seasons has been assessed. The effect of meteorology and other factors in the model has also been assessed. From linear and non-linear model analysis, AOD was a significant parameter in estimating PM2.5. The Random Forest model was the stable model for the study region, with a model R2 of 0.53 and an RMSE of 15.89 μg/m3. The inclusion of meteorological parameters like relative humidity, wind speed, and wind direction decreased the error in prediction by 17.45 μg/m3. The seasonal and spatial analysis indicates that the prediction capability of models varies with stations and seasons. The best performing model was found to be Model RF, and the model could explain about 53.14% of the variability in PM2.5 concentration occurrence in the study region with a prediction error of 15.89 μg/m3. 相似文献
Air pollution is one of the most serious environmental issues faced by humans, and it affects the quality of life in cities. PM2.5 forecasting models can be used to create strategies for assessing and warning the public about anticipated harmful levels of air pollution. Accurate pollutant concentration measurements and forecasting are critical criteria for assessing air quality and are the foundation for making the right strategic decisions. Data-driven machine learning models for PM2.5 forecasting have gained attention in the recent past. In this study, PM2.5 prediction for Hyderabad city was carried out using various machine learning models viz. Multi-Linear Regression (MLR), decision tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), and XGBoost. A deep learning model, the Long Short-Term Memory (LSTM) model, was also used in this study. The results obtained were finally compared based on error and R2 value. The best model was selected based on its maximum R2 value and minimal error. The model's performance was further improved using the randomized search CV hyperparameter optimization technique. Spatio-temporal air quality analysis was initially conducted, and it was found that the average winter PM2.5 concentrations were 68% higher than the concentrations in summer. The analysis revealed that XGBoost regression was the best-performing machine learning model with an R2 value of 0.82 and a Mean Absolute Error (MAE) of 7.01 μg/ m3, whereas the LSTM deep learning model performed better than XGBoost regression for PM2.5 modeling with an R2 value of 0.89 and an MAE of 5.78 μg/ m3. 相似文献
To evaluate the relationship between air pollution and morbidity and mortality in epidemiological studies, the exposure of populations must be defined. Generally, ambient air monitoring networks are the source of the exposure data for these studies. In this study, we developed methods to define population exposure regions that represent minimal variation in air pollutant concentrations. We evaluated the spatial and temporal variation in concentrations for particulate matter less than 2.5 μm (PM2.5) and 10 μm (PM10) and ozone (O3) across New York State. The results from the PM2.5 and ozone analysis indicate a significant degree of regional transport and showed regions of consistent concentrations of 100 and 50 miles, respectively, around each monitor. PM10 analysis indicated little temporal and spatial variation for this pollutant and larger regions were adopted. The exposure characterization regions for PM2.5, PM10, and ozone have been used in ecological epidemiological investigations by the New York State Department of Health. This work was conducted under the Environmental Public Health Tracking grant from the Centers for Disease Control and Prevention. 相似文献
Household air pollution (HAP) due to solid fuel use is a major public health threat in low-income countries. Most health effects are thought to be related to exposure to the fine particulate matter (PM) component of HAP, but it is currently impractical to measure personal exposure to PM in large studies. Carbon monoxide (CO) has been shown in cross-sectional analyses to be a reliable surrogate for particles<2.5 µm in diameter (PM2.5) in kitchens where wood-burning cookfires are a dominant source, but it is unknown whether a similar PM2.5-CO relationship exists for personal exposures longitudinally. We repeatedly measured (216 measures, 116 women) 24-hour personal PM2.5 (median [IQR] = 0.11 [0.05, 0.21] mg/m3) and CO (median [IQR] = 1.18 [0.50, 2.37] mg/m3) among women cooking over open woodfires or chimney woodstoves in Guatemala. Pollution measures were natural-log transformed for analyses. In linear mixed effects models with random subject intercepts, we found that personal CO explained 78% of between-subject variance in personal PM2.5. We did not see a difference in slope by stove type. This work provides evidence that in settings where there is a dominant source of biomass combustion, repeated measures of personal CO can be used as a reliable surrogate for an individual''s PM2.5 exposure. This finding has important implications for the feasibility of reliably estimating long-term (months to years) PM2.5 exposure in large-scale epidemiological and intervention studies of HAP. 相似文献
Ambient fine particulate matter (PM2.5) pollution is currently a major public health concern in Chinese urban areas. However, PM2.5 exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM2.5 mass concentrations for urban residences in Beijing.
Methods
In this study, 24-h real-time indoor and ambient PM2.5 mass concentrations were concurrently collected for 41 urban residences in the non-heating season. The diurnal variation of pollutant concentrations was characterized. Pearson correlation analysis was used to examine the correlation between indoor and ambient PM2.5 mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM2.5 mass concentration.
Results
Hourly ambient PM2.5 mass concentrations were 3–280 μg/m3 with a median of 58 μg/m3, and hourly indoor counterpart were 4–193 μg/m3 with a median of 34 μg/m3. The median indoor/ambient ratio of PM2.5 mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM2.5 mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM2.5 mass concentrations on the community basis (coefficients: r≥0.90, p<0.0001), and the ambient data explained ≥84% variance of the indoor data. Regression analysis suggested that the variables, such as traffic conditions, indoor smoking activities, indoor cleaning activities, indoor plants and number of occupants, had significant influences on the indoor PM2.5 mass concentrations.
Conclusions
PM2.5 of ambient origin made dominant contribution to residential indoor PM2.5 exposure in the non-heating season under the high ambient fine particle pollution condition. Nonetheless, the large inter-residence variability of infiltration factor of ambient PM2.5 raised the concern of exposure misclassification when using ambient PM2.5 mass concentrations as exposure surrogates. PM2.5 of indoor origin still had minor influence on indoor PM2.5 mass concentrations, particularly at 11:00–13:00 and 22:00–0:00. The predictive models suggested that particles from traffic emission, secondary aerosols, particles from indoor smoking, resuspended particles due to indoor cleaning and particles related to indoor plants contributed to indoor PM2.5 mass concentrations in this study. Real-time ventilation measurements and improvement of questionnaire design to involve more variables subject to built environment were recommended to enhance the performance of the predictive models. 相似文献
Fine particulate matter (PM2.5) has been linked to cardiovascular disease, possibly via accelerated atherosclerosis. We examined associations between the progression of the intima-medial thickness (IMT) of the common carotid artery, as an indicator of atherosclerosis, and long-term PM2.5 concentrations in participants from the Multi-Ethnic Study of Atherosclerosis (MESA).
Methods and Results
MESA, a prospective cohort study, enrolled 6,814 participants at the baseline exam (2000–2002), with 5,660 (83%) of those participants completing two ultrasound examinations between 2000 and 2005 (mean follow-up: 2.5 years). PM2.5 was estimated over the year preceding baseline and between ultrasounds using a spatio-temporal model. Cross-sectional and longitudinal associations were examined using mixed models adjusted for confounders including age, sex, race/ethnicity, smoking, and socio-economic indicators. Among 5,362 participants (5% of participants had missing data) with a mean annual progression of 14 µm/y, 2.5 µg/m3 higher levels of residential PM2.5 during the follow-up period were associated with 5.0 µm/y (95% CI 2.6 to 7.4 µm/y) greater IMT progressions among persons in the same metropolitan area. Although significant associations were not found with IMT progression without adjustment for metropolitan area (0.4 µm/y [95% CI −0.4 to 1.2 µm/y] per 2.5 µg/m3), all of the six areas showed positive associations. Greater reductions in PM2.5 over follow-up for a fixed baseline PM2.5 were also associated with slowed IMT progression (−2.8 µm/y [95% CI −1.6 to −3.9 µm/y] per 1 µg/m3 reduction). Study limitations include the use of a surrogate measure of atherosclerosis, some loss to follow-up, and the lack of estimates for air pollution concentrations prior to 1999.
Conclusions
This early analysis from MESA suggests that higher long-term PM2.5 concentrations are associated with increased IMT progression and that greater reductions in PM2.5 are related to slower IMT progression. These findings, even over a relatively short follow-up period, add to the limited literature on air pollution and the progression of atherosclerotic processes in humans. If confirmed by future analyses of the full 10 years of follow-up in this cohort, these findings will help to explain associations between long-term PM2.5 concentrations and clinical cardiovascular events.
Please see later in the article for the Editors'' Summary相似文献
Forest ecosystem plays an important role as carbon sinks in Southwest China. Currently, remote sensing technology has been widely used to substantially model the high temporal and spatial variation in gross primary production (GPP) at a site or regional scale. However, during the growing season, the regional uncertainty of GPP in the forest ecosystem and the relative contributions of climate variations to interannual variation (IAV) of GPP are not well understood across Southwest China. Our research focuses on the joint analysis of the three-cornered hat (TCH) algorithm and uses the contribution index to analyse the model's uncertainties varying with plant functional types (PFTs), climate zones, and the contribution of climate variabilities to GPP IAV. Here, three GPP datasets are used to investigate how climate variabilities contribute to the GPP IAV during the growing season. The uncertainties in GPP vary from 829.33 g C m−2 year−1 to 2031.86 g C m−2 year−1 for different models in different climate zones and different PFTs. Additionally, the results highlight that precipitation dominates the interannual variation in GPP in forest ecosystem during the growing season in Southwest China. It makes the largest contribution (34.46%) to the IAV of GPP in the climate zone of E (cold subtropical highland area) and the largest contribution (80.83%) to PFTs of the MF (mixed forest). Our study suggests the availability and applicability of GPP products can be used to assess GPP uncertainties and analyse the contributions of climate factors to GPP IAV in forest ecosystem or other ecosystems. 相似文献
Exposure to atmospheric particulate matter PM2.5 (aerodynamic diameter ≤ 2.5 μm) has been epidemiologically associated with respiratory illnesses. However, recent data have suggested that PM2.5 is able to infiltrate into circulation and elicit a systemic inflammatory response. Potential adverse effects of air pollutants to the central nervous system (CNS) have raised concerns, but whether PM2.5 causes neurotoxicity remains unclear. In this study, we have demonstrated that PM2.5 impairs the tight junction of endothelial cells and increases permeability and monocyte transmigration across endothelial monolayer in vitro, indicating that PM2.5 is able to disrupt blood–brain barrier integrity and gain access to the CNS. Exposure of primary neuronal cultures to PM2.5 resulted in decrease in cell viability and loss of neuronal antigens. Furthermore, supernatants collected from PM2.5‐treated macrophages and microglia were also neurotoxic. These macrophages and microglia significantly increased extracellular levels of glutamate following PM2.5 exposure, which were negatively correlated with neuronal viability. Pre‐treatment with NMDA receptor antagonist MK801 alleviated neuron loss, suggesting that PM2.5 neurotoxicity is mediated by glutamate. To determine the potential source of excess glutamate production, we investigated glutaminase, the main enzyme for glutamate generation. Glutaminase was reduced in PM2.5‐treated macrophages and increased in extracellular vesicles, suggesting that PM2.5 induces glutaminase release through extracellular vesicles. In conclusion, these findings indicate PM2.5 as a potential neurotoxic factor, crucial to understanding the effects of air pollution on the CNS.
?Ambient fine particulate matter (PM2.5) could induce cardiovascular diseases (CVD), but the mechanism remains unknown. To investigate the roles of epidermal growth factor receptor (EGFR) and NOD‐like receptors (NLRs) in PM2.5‐induced cardiac injury, we set up a BALB/c mice model of PM2.5‐induced cardiac inflammation and fibrosis with intratracheal instillation of PM2.5 suspension (4.0 mg/kg b.w.) for 5 consecutive days (once per day). After exposure, we found that mRNA levels of CXCL1, interleukin (IL)‐6, and IL‐18 were elevated, but interestingly, mRNA level of NLRP12 was significant decreased in heart tissue from PM2.5‐induced mice compared with those of saline‐treated mice using real‐time PCR. Protein levels of phospho‐EGFR (Tyr1068), phospho‐Akt (Thr308), NLRP3, NF‐κB‐p52/p100, and NF‐κB‐p65 in heart tissue of PM2.5‐exposed mice were all significantly increased using immunohistochemistry or Western blotting. Therefore, PM2.5 exposure could induce cardiac inflammatory injury in mice, which may be involved with EGFR/Akt signaling, NLRP3, and NLRP12. 相似文献
Particulate matter with an aerodynamic diameter <2.5 μm (PM2.5) represents a severe environmental problem and is of negative impact on human health. Xi''an City, with a population of 6.5 million, is among the highest concentrations of PM2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM2.5 concentrations were greater than 100 μg/m3. Recently, a few studies have explored the potential causes of high PM2.5 concentration using remote sensing data such as the MODIS aerosol optical thickness (AOT) product. Linear regression is a commonly used method to find statistical relationships among PM2.5 concentrations and other pollutants, including CO, NO2, SO2, and O3, which can be indicative of emission sources. The relationships of these variables, however, are usually complicated and non-linear. Therefore, a generalized additive model (GAM) is used to estimate the statistical relationships between potential variables and PM2.5 concentrations. This model contains linear functions of SO2 and CO, univariate smoothing non-linear functions of NO2, O3, AOT and temperature, and bivariate smoothing non-linear functions of location and wind variables. The model can explain 69.50% of PM2.5 concentrations, with R2 = 0.691, which improves the result of a stepwise linear regression (R2 = 0.582) by 18.73%. The two most significant variables, CO concentration and AOT, represent 20.65% and 19.54% of the deviance, respectively, while the three other gas-phase concentrations, SO2, NO2, and O3 account for 10.88% of the total deviance. These results show that in Xi''an City, the traffic and other industrial emissions are the primary source of PM2.5. Temperature, location, and wind variables also non-linearly related with PM2.5. 相似文献
Susceptibility of patients with chronic obstructive pulmonary disease (COPD) to cardiovascular autonomic dysfunction associated with exposure to metals in ambient fine particles (PM2.5, particulate matter with aerodynamic diameter ≤2.5 µm) remains poorly evidenced. Based on the COPDB (COPD in Beijing) panel study, we aimed to compare the associations of heart rate (HR, an indicator of cardiovascular autonomic function) and exposure to metals in PM2.5 between 53 patients with COPD and 82 healthy controls by using linear mixed-effects models. In all participants, the HR levels were significantly associated with interquartile range increases in the average concentrations of Cr, Zn, and Pb, but the strength of the associations differed by exposure time (from 1.4% for an average 9 days (d) Cr exposure to 3.5% for an average 9 d Zn exposure). HR was positively associated with the average concentrations of PM2.5 and certain metals only in patients with COPD. Associations between HR and exposure to PM2.5, K, Cr, Mn, Ni, Cu, Zn, As, and Se in patients with COPD significantly differed from those in health controls. Furthermore, association between HR and Cr exposure was robust in COPD patients. In conclusion, our findings indicate that COPD could exacerbate difference in HR following exposure to metals in PM2.5.
PM2.5 emissions not only have serious adverse health effects, but also impede transportation activities, especially in air and highway transport. As a result, PM2.5 emissions have become a public policy concern in China in recent years. Currently, the vast majority of existing researches on PM2.5 are based on natural science perspective. Very few economic studies on the subject have been conducted with linear models. This paper adopts provincial panel data from 2001 to 2012, and uses the STIRPAT model and nonparametric additive regression models to examine the key driving forces of PM2.5 emissions in China. The results show that the nonlinear effect of economic growth on PM2.5 emissions is consistent with the Environmental Kuznets Curve (EKC) hypothesis. The nonlinear impact of urbanization exhibits an inverted “U-shaped” pattern due to the rapid development of urban real estate in the early stages and the strengthening of environmental protection measures in the latter stage. Coal consumption follows an inverted “U-shaped” relationship with PM2.5 emissions owing to massive coal consumption at the beginning and efforts to optimize the energy structure as well as technological progress in clean energy in the latter stages. The nonlinear inverted “U-shaped” impact of private vehicles may be due to the different roles of scale, structural and technical effects at different stages. However, energy efficiency improvement follows a positive “U-shaped” pattern in relation to PM2.5 emissions because of differences in the scale of the economy and the speed of technological progress at different times. As a result, the differential dynamic effects of the driving forces of PM2.5 emissions at different times should be taken into consideration when initiating policies to reduce PM2.5 emissions in China. 相似文献
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