Spatio-temporal air quality analysis and PM2.5 prediction over Hyderabad City,India using artificial intelligence techniques

Air pollution is one of the most serious environmental issues faced by humans, and it affects the quality of life in cities. PM_2.5 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 PM_2.5 forecasting have gained attention in the recent past. In this study, PM_2.5 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 PM_2.5 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 PM_2.5 modeling with an R² value of 0.89 and an MAE of 5.78 μg/ m³.     

Seasonal Variation in Schools’ Indoor Air Environments and Health Symptoms among Students in an Eastern Mediterranean Climate

The indoor air quality (IAQ) in classrooms highly affects the health and productivity of students. This article aims to clarify seasonal variation in indoor environment and sick building syndromes (SBS) symptoms in an Eastern Mediterranean climate. A series of field measurements were conducted during the fall and winter seasons from October 2011 to March 2012 in 12 naturally ventilated schools located in the Gaza Strip. Data on environmental perception and health symptoms were obtained from 724 students by using a validated questionnaire. The results showed that indoor PM₁₀ and PM_2.5 concentrations were 426.3 ± 187.6 μg/m³ and 126.6 ± 94.8 μg/m³, respectively. The CO₂ concentrations and ventilation rate widely exceeded their reference values during the winter season. The prevalence rates of general symptoms were relatively high at baseline assessment in the fall season and increased significantly during follow-up in the winter season. Significant increases in disease symptoms such as mucosal irritation and pre-existing asthma symptoms among students could be related to poor indoor air quality. Five distinct groups of SBS symptoms from factor analysis of students’ related symptoms were significantly correlated with PM₁₀ and PM_2.5, CO₂, ventilation rate, and indoor temperature. As vulnerable children, this situation negatively affects their school performance and health.     

Modeling temporal patterns of methane effluxes using multiple regression and random forest in Poyang Lake,China

In this study, we used two statistical models to predict daily CH₄ effluxes and compared the prediction accuracy of two models in Poyang Lake. Statistical models included linear model and Random forest model (RF) which can handle high dimensional non-linear relationships, categorical and continuous predictors, and highly collinear predictor variables. Seven climatic factors and water level data, together with the field CH₄ efflux at monthly intervals from 2011 to 2014 were used for model development and cross-validation. We found that the RF model provided the best prediction accuracy for daily CH₄ effluxes, whereas the linear model gave low prediction accuracy for CH₄ effluxes. The coefficient of determination was 0.93 and 0.63 for the “best” RF and linear models with the same climatic variables, respectively. The “best” linear model had the highest model-performance errors including the mean absolute error, root mean-square error, and the normalized root-mean-square error, followed by the “best” RF models. In addition, cross-validation results for the two “best” models also showed that the RF model was the best model for estimating CH₄ effluxes. We applied the optimum RF model to simulate daily CH₄ effluxes from 1 January 2011 to 31 December 2014, and then estimated the seasonal and annual CH₄ emissions in Poyang Lake. The mean CH₄ efflux in the summer was notably higher than that in the other seasons, with values of 0.097, 0.28, 0.11, and 0.045 mmol m^?2 day^?1 in the spring, in the summer, in the autumn, and in the winter over a 4-year period, respectively. The mean annual emission was 3.13 g m^?2 year^?1, which was considerately lower than the mean global annual emission in lakes and that in the other subtropical lakes of the world. We found that the RF model may be used to estimate CH₄ effluxes and emissions in other lakes in the world.     

Study of PM10, PM2.5, and PM1 levels in during dust storms and local air pollution events in urban and rural sites in Tehran

The aim of this study is to survey the PM₁₀, PM_2.5, and PM₁ concentrations in rural and urban areas in Tehran province during cold, warm and dust storm days from December 22, 2016 to June 5, 2017 using Grimm Model aerosol spectrometer. During the study period, daily PM₁₀, PM_2.5, and PM₁ concentrations ranged from 27.2 to 244.96, 8.4 to 77.9, and 6.5 to 56.8 μg/m³ in urban sites, and 22.8 to 286.4, 6 to 41.1, and 2.1 to 20.2 μg/m³ in rural parts, respectively. Particularly, both daily WHO limits for outdoor PM₁₀ (50.0 μg/m³) and PM_2.5 (25.0 μg/m³) exceeded in 95% and 83% of the outdoor measurements in winter and 82% and 58% in total sampled days in urban site, respectively. The 24-h average PM₁₀ and PM_2.5 concentrations also exceeded by 59% and 18% in winter and by 36% and 14% of all sampling days in rural site, respectively. During the dust storm, the 24-h average PM₁₀, PM_2.5, and PM₁ concentrations were, respectively 4.7, 2, and 1.96 times higher than those in urban site and 2, 1.7, and 1.3 times more than those in rural site in all sampled days.     

The modeling and health risk assessment of PM2.5 from Tema Oil Refinery

Fine particulate matters (PM_2.5) are known to pose serious health problems compared to other air pollutants. The current study employed air dispersion modeling system (AERMOD) to simulate the concentration of PM_2.5 from Tema Oil Refinery (TOR) and to assess the non-cancer risk and mortalities of the exposed population. In addition, the effects of local climatic factors on the distribution and concentration of PM_2.5 within the three main seasons (Major Raining Season (MRS), Low Raining Season (LRS) and Dry Season (DS)) were investigated. The AERMOD results showed that both 24-h (38.8 µg m^?3) and annual (12.6 µg m^?3) PM_2.5 concentration levels were in exceedance of the international limits. However, a decreasing trend in seasonal PM_2.5 concentrations was observed. Health risk assessment (HRA), indicated by hazard index (HI), revealed that the amount of Al₂O₃ present in the PM_2.5 caused a significant non-carcinogenic health risk to the exposed population (both adults and children) within the Metropolis (HI = 2.4 for adults and HI = 1.5 for children). Additionally, cardiopulmonary disease related mortalities due to PM_2.5 exposure (181 deaths for adults and 24 deaths for children) were found high compared to deaths caused by lung cancer (137 deaths for adults and 16 deaths for children).     

Acute and chronic effects of particles on hospital admissions in New-England

Background

Many studies have reported significant associations between exposure to PM_2.5 and hospital admissions, but all have focused on the effects of short-term exposure. In addition all these studies have relied on a limited number of PM_2.5 monitors in their study regions, which introduces exposure error, and excludes rural and suburban populations from locations in which monitors are not available, reducing generalizability and potentially creating selection bias.

Methods

Using our novel prediction models for exposure combining land use regression with physical measurements (satellite aerosol optical depth) we investigated both the long and short term effects of PM_2.5 exposures on hospital admissions across New-England for all residents aged 65 and older. We performed separate Poisson regression analysis for each admission type: all respiratory, cardiovascular disease (CVD), stroke and diabetes. Daily admission counts in each zip code were regressed against long and short-term PM_2.5 exposure, temperature, socio-economic data and a spline of time to control for seasonal trends in baseline risk.

Results

We observed associations between both short-term and long-term exposure to PM_2.5 and hospitalization for all of the outcomes examined. In example, for respiratory diseases, for every10-µg/m³ increase in short-term PM_2.5 exposure there is a 0.70 percent increase in admissions (CI = 0.35 to 0.52) while concurrently for every10-µg/m³ increase in long-term PM_2.5 exposure there is a 4.22 percent increase in admissions (CI = 1.06 to 4.75).

Conclusions

As with mortality studies, chronic exposure to particles is associated with substantially larger increases in hospital admissions than acute exposure and both can be detected simultaneously using our exposure models.     

地表植被景观对PM2.5浓度空间分布的影响研究

我国当前城市日益频发的雾霾问题引发公众广泛关注,PM_2.5被认为是雾霾的主要成因。研究认为,在某一区域短时间尺度上（如日）,PM_2.5浓度主要受气象条件影响。但在较长时间尺度上（如季,年）,由于气象条件基本相似,则PM_2.5浓度主要受土地利用特别是地表植被景观的影响。如何耦合地表植被景观格局与PM_2.5浓度信息,定量分析其影响是当前相关科学研究的一个难点,需要引入新思路。首先基于季节气象条件基本相似的科学假设,采用土地利用回归模型分四季高精度模拟PM_2.5浓度空间分布。其次,采用像元二分模型分四季估算研究区植被覆盖度。在此基础上采用随机抽样法通过统计回归模型耦合植被覆盖度与PM_2.5空间分布,定量研究植被覆盖度对PM_2.5分布影响及其尺度效应。研究结果表明：1）植被覆盖度与PM_2.5浓度在本研究选择的空间尺度上,都显著负相关,说明植被覆盖度对PM_2.5具有显著影响;同一个季节不同尺度上,以及不同季节同一尺度上的植被覆盖度对PM_2.5浓度的影响存在一定差异。2）植被覆盖度对PM_2.5浓度的影响方式比较复杂,不同的季节的表现方式不同,总体来说PM_2.5浓度与植被覆盖度曲线回归模型的拟合度高于线性回归模型,说明植被覆盖度对PM_2.5的影响具有非线性特征。3）不同的PM_2.5浓度水平下,植被覆盖度对PM_2.5浓度的影响程度存在差异。PM_2.5浓度越高,植被覆盖度对其浓度的影响越明显。本研究提出的区域尺度耦合地表植被覆盖与PM_2.5浓度的思路与方法,有效的揭示了植被覆盖度对PM_2.5浓度分布的影响方式与尺度效应,为通过优化城市植被缓解大气污染提供一定参考。     

Characterizing the Indoor-Outdoor Relationship of Fine Particulate Matter in Non-Heating Season for Urban Residences in Beijing

Objective

Ambient fine particulate matter (PM_2.5) pollution is currently a major public health concern in Chinese urban areas. However, PM_2.5 exposure primarily occurs indoors. Given such, we conducted this study to characterize the indoor-outdoor relationship of PM_2.5 mass concentrations for urban residences in Beijing.

Methods

In this study, 24-h real-time indoor and ambient PM_2.5 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 PM_2.5 mass concentrations. Regression analysis with ordinary least square was employed to characterize the influences of a variety of factors on PM_2.5 mass concentration.

Results

Hourly ambient PM_2.5 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 PM_2.5 mass concentration was 0.62. The diurnal variation of residential indoor and ambient PM_2.5 mass concentrations tracked with each other well. Strong correlation was found between indoor and ambient PM_2.5 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 PM_2.5 mass concentrations.

Conclusions

PM_2.5 of ambient origin made dominant contribution to residential indoor PM_2.5 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 PM_2.5 raised the concern of exposure misclassification when using ambient PM_2.5 mass concentrations as exposure surrogates. PM_2.5 of indoor origin still had minor influence on indoor PM_2.5 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 PM_2.5 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.     

Particulate matter and bacteria characteristics of the Middle East Dust (MED) storms over Ahvaz,Iran

The Middle East Dust storms have greatly affected the south and west parts of Iran during the last decade. The main purpose of this study was to examine and compare culturable airborne bacteria concentration in particulate matter (PM) during normal, semi-dust, and dust event days in different places and seasons in Ahvaz from November 2011 to May 2012. Sampling was performed every 6 days and on dust event days at different sampling stations. The overall mean concentrations of PM₁₀, PM_2.5, and PM₁ for the entire study period were 598.92, 114.8, and 34.5 μg/m³, respectively. The PM concentrations during the dust event days were much higher than normal and semi-dust event days. The highest mean PM concentrations were observed in March 2011. The low PM_2.5/PM₁₀ ratios indicate that these PM are mostly originating from natural sources such as dust storms. The overall mean concentration of total bacteria during the study period was 620.6 CFU/m³. The greatest bacterial concentrations were observed during dust event days and at areas with high traffic and more human activities compared with normal days and greener areas. The percentage of gram-positive bacteria was significantly higher than that during the study period (89 vs 11 %). During this study, 26 genera of culturable bacteria were identified from all the sampling stations. The most dominant genera in all sampling stations were Streptomyces, Bacillus, Kocuria, Corynebacterium, and Paenibacillus. The results also showed that there were positive correlations between PM and bacterial concentrations during the study period (p < 0.05).     

Attribution of seasonal leaf area index trends in the northern latitudes with “optimally” integrated ecosystem models

Significant increases in remotely sensed vegetation indices in the northern latitudes since the 1980s have been detected and attributed at annual and growing season scales. However, we presently lack a systematic understanding of how vegetation responds to asymmetric seasonal environmental changes. In this study, we first investigated trends in the seasonal mean leaf area index (LAI) at northern latitudes (north of 30°N) between 1982 and 2009 using three remotely sensed long‐term LAI data sets. The most significant LAI increases occurred in summer (0.009 m² m^?2 year^?1, p < .01), followed by autumn (0.005 m² m^?2 year^?1, p < .01) and spring (0.003 m² m^?2 year^?1, p < .01). We then quantified the contribution of elevating atmospheric CO₂ concentration (eCO₂), climate change, nitrogen deposition, and land cover change to seasonal LAI increases based on factorial simulations from 10 state‐of‐the‐art ecosystem models. Unlike previous studies that used multimodel ensemble mean (MME), we used the Bayesian model averaging (BMA) to optimize the integration of model ensemble. The optimally integrated ensemble LAI changes are significantly closer to the observed seasonal LAI changes than the traditional MME results. The BMA factorial simulations suggest that eCO₂ provides the greatest contribution to increasing LAI trends in all seasons (0.003–0.007 m² m^?2 year^?1), and is the main factor driving asymmetric seasonal LAI trends. Climate change controls the spatial pattern of seasonal LAI trends and dominates the increase in seasonal LAI in the northern high latitudes. The effects of nitrogen deposition and land use change are relatively small in all seasons (around 0.0002 m² m^?2 year^?1 and 0.0001–0.001 m² m^?2 year^?1, respectively). Our analysis of the seasonal LAI responses to the interactions between seasonal changes in environmental factors offers a new perspective on the response of global vegetation to environmental changes.     

Influence of meteorological parameters and PM2.5 on the level of culturable airborne bacteria and fungi in Abadan,Iran

In recent years, monitoring of airborne bacteria and fungi concentrations has obtained increasing universal attraction not only for influences on ecological balance but also for evaluating their public health consequences. In this study, we aimed to investigate culturable airborne bacteria and fungi levels in different sites of Abadan, and their association with meteorological parameters and PM_2.5 levels. Abadan is one of the most industrialized cities in the southwest of Iran where over the current decade has experienced lots of dust storm episodes. In total, 400 air samples were collected in 6 months (autumn and winter) using a single-stage viable Andersen cascade impactor for sampling airborne bacteria and fungi and portable DustTrak Aerosol Monitor 8520 for measuring PM_2.5 concentrations and meteorological parameters. Microbial concentrations showed a significant difference between various sites over the study period with averages of 569.57?±?312.64 and 482.73?±?242.86 CFU/M³ for bacteria and fungi, respectively. The air temperature had a significant effect on the concentration of both airborne bacteria and fungi. A significant positive correlation between relative humidity and fungi but no correlation between relative humidity and bacteria concentrations were observed. The average airborne PM_2.5 concentrations of all sites among the study period was 93.24?±?116.72 μg/m³. The atmospheric bacterial and fungal communities were strongly positively correlated with the ambient PM_2.5 level. The levels of airborne bacteria and fungi along with PM_2.5 in the air of the city were relatively higher than the recommended levels. Therefore, the best course of action is needed to control emission sources. Further studies are also needed to evaluate the clinical analysis of the health effects of exposure to these pollutants.

     

Air pollution and infant mortality: Evidence from China

Using a newly assembled prefecture-city level dataset from 2004 to 2015, this paper examines the impact of air pollution on child mortality in China. To identify the causal effect, we exploit ventilation coefficient as the instrument for urban air pollution. We find that a 10 μg/m³ increase in annual PM_2.5 concentration causes 163 infant deaths per 100,000 live births per year in a city.     

Fine Particulate Air Pollution and Hospital Emergency Room Visits for Respiratory Disease in Urban Areas in Beijing,China, in 2013

BackgroundHeavy fine particulate matter (PM_2.5) air pollution occurs frequently in China. However, epidemiological research on the association between short-term exposure to PM_2.5 pollution and respiratory disease morbidity is still limited. This study aimed to explore the association between PM_2.5 pollution and hospital emergency room visits (ERV) for total and cause-specific respiratory diseases in urban areas in Beijing.MethodsDaily counts of respiratory ERV from Jan 1 to Dec 31, 2013, were obtained from ten general hospitals located in urban areas in Beijing. Concurrently, data on PM_2.5 were collected from the Beijing Environmental Protection Bureau, including 17 ambient air quality monitoring stations. A generalized-additive model was used to explore the respiratory effects of PM_2.5, after controlling for confounding variables. Subgroup analyses were also conducted by age and gender.ResultsA total of 92,464 respiratory emergency visits were recorded during the study period. The mean daily PM_2.5 concentration was 102.1±73.6 μg/m³. Every 10 μg/m³ increase in PM_2.5 concentration at lag₀ was associated with an increase in ERV, as follows: 0.23% for total respiratory disease (95% confidence interval [CI]: 0.11%-0.34%), 0.19% for upper respiratory tract infection (URTI) (95%CI: 0.04%-0.35%), 0.34% for lower respiratory tract infection (LRTI) (95%CI: 0.14%-0.53%) and 1.46% for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) (95%CI: 0.13%-2.79%). The strongest association was identified between AECOPD and PM_2.5 concentration at lag_0-3 (3.15%, 95%CI: 1.39%-4.91%). The estimated effects were robust after adjusting for SO₂, O₃, CO and NO₂. Females and people 60 years of age and older demonstrated a higher risk of respiratory disease after PM_2.5 exposure.ConclusionPM_2.5 was significantly associated with respiratory ERV, particularly for URTI, LRTI and AECOPD in Beijing. The susceptibility to PM_2.5 pollution varied by gender and age.     

Heavy Metal Content in PM2.5 Air Samples Collected in the Metropolitan Area of Monterrey,México

The air PM_2.5 concentration and its heavy metal content (Fe, Pb, Mn, Ni, As) were measured in the Metropolitan Area of Monterrey, Méxicoin Mexico, an area that is characterized by both very active and diverse industrial activity and intense highway traffic and industrial activity. The 24-h PM_2.5 samples were collected in two different zones during a 1-year-long measurement program (February 2008–February 2009). The year PM_2.5 average was above 15 μg/m³ exceeding Mexican and international standards. The difference of PM_2.5 in each zone was not statistically significant. The greatest metal content was for iron, followed by lead, manganese, nickel and arsenic. The difference in metal content for Pb, Mn, and As was statistically significant.     

An Assessment of Risk from Particulate Released from Outdoor Wood Boilers

Use of outdoor wood boilers (OWB) has increased due to cost of fossil fuels. OWB short stacks release particles close to the breathing level, producing high levels of particulate matter ≤2.5 μm in diameter (PM_2.5). This assessment determines OWB contribution to local cancer risk and estimates thresholds for acute non-cancer risks. Carcinogenic PAHs in wood smoke (PM_2.5) cancer risks range from 2.7 × 10^–3 for the upper bound scenario (95% UCL value of PM_2.5 (665 μg/m³)) to 7.6 × 10^–5 for the lower bound (mean (186 μg/m³)). These risks represent a 7-fold increase of acceptable cancer risk for the lower bound value and 2 orders of magnitude above acceptable levels for the upper bound values. Non-cancer effects such as asthma and cardiopathies include respiratory attacks, hospital emergency room visits, and hospitalizations. Inhaled dose acute risk thresholds of 96, 120, and 250 μg PM 2.5/6 hours are proposed. Operation of an OWB that emits 100 grams PM_2.5/h was modeled and found to increase the exposures that exceed the 120-μg-risk level at and in residences within 500 to 1000 feet. The increases are projected to occur during periods of poor air mixing due to decreased wind speeds or inversions. Our analysis proposes a 6-h PM_2.5 inhaled dose threshold to predict peak periods of unhealthy air quality instead of 24-h and annual averages standards, which mask peak emissions.     

Particle size distribution of the major Alternaria alternata allergen,Alt a 1, derived from airborne spores and subspore fragments

Fungal fragments are abundant immunoreactive bioaerosols that may outnumber the concentrations of intact spores in the air. To investigate the importance of Alternaria fragments as sources of allergens compared to Alternaria spores, we determined the levels of Alternaria spores and Alt a 1 (the major allergen in Alternaria alternata spores) collected on filters within three fractions of particulate matter (PM) of different aerodynamic diameter: (1) PM_>10, (diameter>10 μm); (2) PM_2.5-10 (2.5–10μm); (3) PM_2.5 (0.12–2.5 μm). The airborne particles were collected using a three stage high-volume ChemVol cascade impactor during the Alternaria sporulation season in Poznań, Poland (30 d between 6 July and 22 September 2016). The quantification of Alt a 1 was performed using the enzyme-linked immunosorbent assay. High concentrations of Alt a 1 were recorded during warm and dry d characterized by high sunshine duration, lack of clouds and high dew point values. Atmospheric concentrations of Alternaria spores correlated significantly (r = 0.930, p < 0.001) with Alt a 1 levels. The highest Alt a 1 was recorded in PM_2.5-10 (66.8 % of total Alt a 1), while the lowest in PM_2.5 (<1.0 %). Significantly more Alt a 1 per spore (>30 %) was observed in PM_2.5-10 than in PM_>10. This Alt a 1 excess may be derived from sources other than spores, e.g. hyphal fragments. Overall, in outdoor air the major source of Alt a 1 are intact Alternaria spores, but the impact of other fungal fragments (hyphal parts, broken spores, conidiophores) cannot be neglected, as they may increase the total atmospheric Alt a 1 concentration.     

Estimating PM2.5 Concentrations in Xi'an City Using a Generalized Additive Model with Multi-Source Monitoring Data

Particulate matter with an aerodynamic diameter <2.5 μm (PM_2.5) 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 PM_2.5 in China. In 2013, in total, there were 191 days in Xi’an City on which PM_2.5 concentrations were greater than 100 μg/m³. Recently, a few studies have explored the potential causes of high PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5. Temperature, location, and wind variables also non-linearly related with PM_2.5.     

Acute Effects of Particulate Air Pollution on the Incidence of Coronary Heart Disease in Shanghai,China

Introduction

Evidence based on ecological studies in China suggests that short-term exposure to particulate matter (PM) is associated with cardiovascular mortality. However, there is less evidence of PM-related morbidity for coronary heart disease (CHD) in China. This study aims to investigate the relationship between acute PM exposure and CHD incidence in people aged above 40 in Shanghai.

Methods

Daily CHD events during 2005–2012 were identified from outpatient and emergency department visits. Daily average concentrations for particulate matter with aerodynamic diameter less than 10 microns (PM₁₀) were collected over the 8-year period. Particulate matter with aerodynamic diameter less than 2.5 microns (PM_2.5) were measured from 2009 to 2012. Analyses were performed using quasi-poisson regression models adjusting for confounders, including long-term trend, seasonality, day of the week, public holiday and meteorological factors. The effects were also examined by gender and age group (41–65 years, and >65 years).

Results

There were 619928 CHD outpatient and emergency department visits. The average concentrations of PM₁₀ and PM_2.5 were 81.7μg/m³ and 38.6μg/m³, respectively. Elevated exposure to PM₁₀ and PM_2.5 was related with increased risk of CHD outpatients and emergency department visits in a short time course. A 10 μg/m³ increase in the 2-day PM₁₀ and PM_2.5 was associated with increase of 0.23% (95% CI: 0.12%, 0.34%) and 0.74% (95% CI: 0.44%, 1.04%) in CHD morbidity, respectively. The associations appeared to be more evident in the male and the elderly.

Conclusion

Short-term exposure to high levels of PM₁₀ and PM_2.5 was associated with increased risk of CHD outpatient and emergency department visits. Season, gender and age were effect modifiers of their association.     

PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing,China

Objective

Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM_2.5) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM_2.5 from August 2013 to July 2014 in Beijing, and to assess the relationship between PM_2.5 and meteorological factors.

Methods

Daily and hourly PM_2.5 data from the Beijing Environmental Protection Bureau (BJEPB) were analyzed separately. Ordinary kriging (OK) interpolation, time-series graphs, Spearman correlation coefficient and coefficient of divergence (COD) were used to describe the spatiotemporal variations of PM_2.5. The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM_2.5 levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM_2.5 and meteorological variables were analyzed using the generalized additive mixed model (GAMM).

Results

Annual mean and median of PM_2.5 concentrations were 88.07 μg/m³ and 71.00 μg/m³, respectively, from August 2013 to July 2014. PM_2.5 concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167). Day to day variation of PM_2.5 showed a long-term trend of fluctuations, with 2–6 peaks each month. PM_2.5 concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM_2.5 concentration using the GAMM model (R ² = 0.59, AIC = 7373.84).

Conclusion

PM_2.5 pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM_2.5 concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM_2.5, whereas relative humidity and air pressure three days earlier are positively correlated with PM_2.5.