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.     

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.     

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.     

Collection and characterization of a bulk PM2.5 air particulate matter material for use in reference materials

A contemporary PM_2.5 (particulate matter smaller than 2.5 Μm aerodynamic diameter) aerosol material from an urban site has been collected for the production of a new standard reference material that will be made available for the development of new PM_2.5 air quality standards. Air particulate matter corresponding to the PM_2.5 fraction was collected at an established Environmental Protection Agency monitoring site in Baltimore, Maryland. The air-sampling system that has been constructed for this collection separates fine particles with a cyclone separator and deposits them onto an array of Teflon membrane filters. The fine air particulate material is removed by ultrasonication or by mechanical means and collected for further preparation of standards. The composition of the collected PM_2.5 aerosol, as well as the composition of the deposited PM_2.5 aerosol, are determined by instrumental nuclear activation analysis and other techniques.     

Estimating PM2.5 concentrations with statistical distribution techniques for health risk assessment in Bangkok

Abstract

Ambient PM_2.5 data in the Central Business District (CBD) of Bangkok monitored by Pollution Control Department and Bangkok Metropolitan Administration were collected over three years in Bangkok from 2015 to 2017. The other air pollutions data were used as the dependent variables to develop mathematic models with statistical distribution technique. Multiple linear regression technique was selected as the main statistical distribution methodology for estimating PM_2.5 concentrations in non-monitored areas. The predicted PM_2.5 concentrations were validated against the measured PM_2.5 concentrations by various statistical techniques. The validation found that the model had strong significant correlations for ambient and roadside area with R ²?=?0.88 and 0.96, respectively. The non-carcinogenic health risk assessment of PM_2.5 was quantified as the hazard quotient (HQ) from both the measured and predicted data. The risk areas and HQ were compared using the inverse distance weighting interpolation technique and illustrated as GIS-based maps. During December to February, the HQ values of PM_2.5 were exceed 1 (HQs?>?1) at all area of CBD; however, the highest HQ was found in the southern part of CBD. The finding could be used for residential health awareness in that area.     

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.     

大气颗粒物(PM2.5、PM10)对地表景观结构的响应研究进展

颗粒物PM_(2.5)、PM_(10)是近年来我国大气首要污染物,威胁环境和人类健康。地表景观结构直接或间接影响PM_(2.5)、PM_(10)浓度,了解其影响过程和机理对于改善生态环境具有重要意义。系统总结了国内外关于PM_(2.5)、PM_(10)对地表景观结构响应的研究成果,指出研究中出现不确定性的可能影响因素,并对今后的发展方向进行展望。得出基本结论:(1)地表景观类型的构成及其格局显著影响大气颗粒物浓度,对PM_(2.5)、PM_(10)起到"源"和"汇"的作用。(2)地表景观结构引起局地气候变化并影响颗粒物的迁移转化,但其影响过程和机理复杂,研究结论并不明确。(3)颗粒物浓度和地表景观数据主要通过实际监测或遥感处理方法获得,但因为获取方法、监测点微观环境及遥感影像等因素影响,导致数据具有不确定性,加上时空尺度相对应的复杂性,大大限制了地表景观结构与PM_(2.5)、PM_(10)响应关系的研究进展,是未来要突破的难点。(4)PM_(2.5)、PM_(10)对地表景观结构响应的区域时空差异及过程,局地小气候变化对颗粒物浓度的影响过程和强度,主要景观类型尤其是水体、湿地景观对大气颗粒物浓度的影响过程、机理与贡献程度等是未来需要关注的方向。     

Fine Particulate Matter and Incident Cognitive Impairment in the REasons for Geographic and Racial Differences in Stroke (REGARDS) Cohort

Studies of the effect of air pollution on cognitive health are often limited to populations living near cities that have air monitoring stations. Little is known about whether the estimates from such studies can be generalized to the U.S. population, or whether the relationship differs between urban and rural areas. To address these questions, we used a satellite-derived estimate of fine particulate matter (PM_2.5) concentration to determine whether PM_2.5 was associated with incident cognitive impairment in a geographically diverse, biracial US cohort of men and women (n = 20,150). A 1-year mean baseline PM_2.5 concentration was estimated for each participant, and cognitive status at the most recent follow-up was assessed over the telephone using the Six-Item Screener (SIS) in a subsample that was cognitively intact at baseline. Logistic regression was used to determine whether PM_2.5 was related to the odds of incident cognitive impairment. A 10 µg/m³ increase in PM_2.5 concentration was not reliably associated with an increased odds of incident impairment, after adjusting for temperature, season, incident stroke, and length of follow-up [OR (95% CI): 1.26 (0.97, 1.64)]. The odds ratio was attenuated towards 1 after adding demographic covariates, behavioral factors, and known comorbidities of cognitive impairment. A 10 µg/m³ increase in PM_2.5 concentration was slightly associated with incident impairment in urban areas (1.40 [1.06–1.85]), but this relationship was also attenuated after including additional covariates in the model. Evidence is lacking that the effect of PM_2.5 on incident cognitive impairment is robust in a heterogeneous US cohort, even in urban areas.     

中国主要城市森林对大气PM2.5的削减量及其占大气PM2.5总量比例

空气中的细颗粒物(PM_2.5)是我国城市空气污染的主要污染物之一,严重威胁着城市居民的健康,限制城市发展的可持续性。PM_2.5去除的自然途径有两种,分别是干沉降和湿沉降,其中干沉降占据主导作用,且干沉降的过程和效率与城市森林紧密关联。目前针对城市森林对干沉降作用的研究主要是在小尺度中从不同树种、不同群落结构、不同景观类型等角度来估算并比较其滞尘量,较少关注其占空气污染总量的比率,从而可能影响对城市森林滞尘服务能力的判断。因此,利用城市森林效益(Urban Forest Effect, UFORE)模型中的大气污染干沉降模块的核心算法,以2015年为例,估算了我国主要城市辖区的城市森林一年内对大气中的PM_2.5削减量以及其占空气中PM_2.5污染总量的比重。结果显示：(1)2015年全国主要城市单位绿地面积日均滞尘量较高地区主要集中在华北地区、华东地区、以及东北地区。其中北京30.47mg/m²,苏州24.63mg/m²,沈阳28.55mg/m²     

北京湿地削减大气PM2.5的生态系统服务流研究

湿地对大气颗粒物的沉降和运移有一定的影响。目前,相关研究多集中在小尺度定量湿地下垫面的颗粒物沉降速度与沉降量以及湿地植物对颗粒物的吸附或阻滞作用。但由于方法的缺失,研究无法在大尺度上定量解释有关湿地对颗粒物的产生、转移、转化和传递过程,这也导致研究结果对政策制定和城市空间规划缺少指导作用。因此,本研究引入了生态系统服务流模型的理论框架,结合颗粒物干沉降与HYSPLIT模型,量化北京市湿地削减PM_2.5服务的物理流量、流动路径以及受益地区。研究结果表明：（1）北京市湿地2015年PM_2.5沉降总量为4240 t,单位面积的平均沉降量为8.27×10^-3 kg/m²;2018年为2610 t,单位面积的平均沉降量为4.46×10^-3 kg/m²;（2）2015年和2018年北京市湿地削减PM_2.5生态系统服务物理流量最高值均出现在冬季,最低值出现在夏季,总体呈现出冬季 > 春季 > 秋季 > 夏季的趋势;（3）2015年和2018年向华北地区迁移的气流轨迹占当季气流轨迹总数的比例最大,京津冀地区与山东省为主要受益区。研究结果既为科学管理湿地资源和实现区域可持续发展提供客观准确的依据,也可为开展湿地生态系统服务流的相关研究奠定一定的基础。     

广州大夫山雨季林内外空气TSP和PM2.5浓度及水溶性离子特征

采用平行同步采样法,于2012年雨季,对广州市大夫山森林公园林内外空气的总悬浮颗粒物(TSP)和细颗粒物(PM_2.5)样品进行了24 h收集,测定了TSP和PM_2.5的质量浓度并分析了样品中水溶性无机离子成分。结果表明:林内外PM_2.5的质量浓度平均值分别为(40.18±10.47)和(55.79±13.01) g/cm³;林内外TSP的质量浓度分别为(101.32 ± 33.19)和(116.61±35.36) g/cm³。林内与林外比,PM_2.5和TSP平均质量浓度都显著减少(P < 0.05),表明森林能显著改善空气环境质量。TSP和PM_2.5中SO₄^2-、Na⁺、NH₄⁺和NO₃^-为水溶性无机离子主要成分,占总离子质量的80%以上,林外这些离子的浓度高于林内(NH₄⁺除外)。这4种离子雨季在空气中的主要存在方式为NaCl、Na₂SO₄、NH₄HSO₄和NH₄NO₃。计算表明,采样期间海盐对大夫山空气TSP和PM_2.5的水溶性组分中Na⁺和Cl^-贡献最大,其它元素主要源自陆地源。林内外TSP和PM_2.5的c(NO₃^-)/c(SO₄^2-)比值在0.3以下,表明固定源是大夫山森林公园空气主要污染贡献者,TSP中c(NO₃^-)/c(SO₄^2-)的比值大于PM_2.5的比值,说明移动源对TSP的贡献大于PM_2.5。     

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.     

Characterization of metal(loid)s in indoor and outdoor PM2.5 of an office in winter period

The characterization of indoor (a naturally ventilated office) and outdoor (adjacent courtyard) metals in PM_2.5 during a winter period in Xi'an, China were carried out. The results indicated that the average mass concentrations of PM_2.5 in indoor and outdoor environments all exceeded the daily average limit of 75 µg m^–3 set by the Chinese government. The dominant metals in PM_2.5 were Ca, Al, Zn, Mg, Fe, and Pb in both indoor and outdoor air. Concentration of As was much higher than the standard of 6 ng m^–3 issued by the government. Enrichment factor analysis showed that anthropogenic emissions might be the primary sources of As, Cd, Pb, and Zn, while crust was the main origin of Co. A majority of indoor-to-outdoor concentration ratios of metal were lower than 1 indicating mostly the contribution of outdoor sources rather than indoor ones. As and Cr in both indoor and outdoor air posed the highest noncarcinogenic and carcinogenic risks, respectively. The noncarcinogenic and carcinogenic risks were 2.74 and 2.54 × 10^?4 indoor and 4.04 and 3.87 × 10^?4 outdoor, which suggested that possible adverse health effects should be of concern.     

基于城市绿地滞尘模型的上海市绿色空间滞留PM2.5功能评估

绿色空间对大气颗粒物有一定吸收滞留作用,是改善空气环境质量与维护城市生态安全的重要区域。该文基于高分2号卫星影像识别2017年上海市绿色空间,并利用城市绿地滞尘模型,结合上海市降水、风速等气象数据与空气质量监测数据,评估了绿色空间滞留PM_2.5功能及其差异。结果表明:2017年上海市绿色空间面积3354 km²,可滞留PM_2.5 3533 t,约合单位面积滞留PM_2.5 10.5 kg hm^-2 a^-1。从绿色空间类型来看,林地滞留PM_2.5能力最强,可达20.2 kg hm^-2 a^-1,远高于草地9.1 kg hm^-2 a^-1和农田8.7 kg hm^-2 a^-1的滞留能力。从季节差异来看,绿色空间夏季滞留PM_2.5能力最高,然后依次为秋季、春季和冬季。从植被分布格局来看,林草地和农...     

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.     

Spatio-temporal modelling of particulate matter concentrations using satellite derived aerosol optical depth over coastal region of Chennai in India

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 PM_2.5 demonstrates that AOD can be used as a proxy for estimating PM_2.5 in the study region as the occurrence of AOD almost resonates with that of PM_2.5. The seasonal variation of AOD and PM_2.5 revealed that during the winter (October–January) and summer (February–May) seasons, AOD reasonably followed the trend of PM_2.5. 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 PM_2.5 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 PM_2.5. 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 PM_2.5 concentration occurrence in the study region with a prediction error of 15.89 μg/m³.     

川渝地区空气质量时空分布格局及影响因素

川渝地区尤其是四川盆地已成为我国空气污染最严重的地区之一,基于2018—2019年川渝地区128个城市站和71个县级站空气质量监测及自然与社会经济数据,采用全局和局部莫兰指数分析了川渝地区空气质量指数(AQI)和不同空气质量分指数(IAQI)的时空格局,并采用偏最小二乘回归(PLSR)从较为宏观的尺度综合分析了川渝地区空气污染的主要驱动因素。研究结果表明：(1)川渝地区空气质量整体为良,主要污染物为O₃,其次为PM₁₀和PM_2.5。盆地区与高原区的主要污染物分别为PM_2.5和O₃;(2)AQI及PM_2.5、PM₁₀、NO₂呈“U”型变化,春冬季最高,夏秋季最低;O₃则在内部两区域都大致呈倒“U”型变化,但峰值分布时间与持续时长明显不同;SO₂和CO年内无明显变化;(3)各污染物具有明显的空间聚集性特征,AQI及PM₁₀、PM_2.5     

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.     

Air quality and its response to satellite-derived urban form in the Yangtze River Delta,China

Air pollution is one of the top environmental concerns and causes of deaths and various diseases worldwide. An important question for sustainable development is to what extent urban design can improve or degrade urban air quality. In this article, we explored the relationship between ground-based observations of air pollution and urban form in the Yangtze River Delta (YRD), the largest metropolitan region in China. We analyzed six criteria pollutants (SO₂, NO₂, PM₁₀, PM_2.5, CO, O₃) and summarized metric (air quality index, AQI) from 129 ambient air quality monitoring stations during 2015. Urban form was characterized using six spatial metrics, including the size, shape, regularity, fragmentation and traffic coupling factor of urban patches, based on satellite-derived land cover data. The results indicated that: (1) PM_2.5, PM₁₀ and O₃ were three primary pollutants in the YRD. The annual average AQI was 79, and the air quality was “moderate” for human health, with the highest and lowest AQI appeared in winter (107) and summer (60). Moreover, the air quality of the southern areas (Zhejiang province, AQI: 68) was generally better than the northern parts (Jiangsu province, AQI: 86). (2) Through the size and shape of urban patches, urban form had a significant effect on urban air quality in the YRD. PARA_MN (Mean Perimeter-area ratio), ENN_MN (Mean Euclidean Nearest Neighbor Distance), CA (Total Urban Area) and NP (Number of urban patches) had the most significant impacts on air quality. PM₁₀ and PM_2.5 were two important pollutants highly positively related to CA and NP, while negatively related to PARA_MN and ENN_MN. In addition, the polycentric urban form was associated with high air quality. (3) Land use configuration was an important indicator to describe the urban air quality. When buffer distance of spatial scale was 25 km, air quality showed the highest correlation with forest coverage. A high forest coverage rate contributed to the better air quality, increasing or preserving the forested areas would help mitigate the air pollution.     

Promoted Relationship of Cardiovascular Morbidity with Air Pollutants in a Typical Chinese Urban Area

BackgroundA large number of studies about effects of air pollutants on cardiovascular mortality have been conducted; however, those investigating association between air pollutants and cardiovascular morbidity are limited, especially in developing countries.MethodsA time-series analysis on the short-term association between outdoor air pollutants including particulate matter (PM) with diameters of 10 µm or less (PM₁₀), sulfur dioxide (SO₂) and nitrogen dioxide (NO₂) and cardiovascular morbidity was conducted in Tianjin, China based on 4 years of daily data (2008–2011). The morbidity data were stratified by sex and age. The effects of air pollutants during the warm season and the cool season were also analyzed separately.ResultsEach increase in PM₁₀, SO₂, and NO₂ by increments of 10 µg/m³ in a 2-day average concentration was associated with increases in the cardiovascular morbidity of 0.19% with 95 percent confidence interval (95% CI) of 0.08–0.31, 0.43% with 95% CI of 0.03–0.84, and 0.52% with 95% CI of −0.09–1.13, respectively. The effects of air pollutants were more evident in the cool season than those in the warm season, females and the elderly were more vulnerable to outdoor air pollution.ConclusionsAll estimated coefficients of PM₁₀, SO₂ and NO₂ are positive but only the effect of SO₂ implied statistical significance at the 5% level. Moreover, season, sex and age might modify health effects of outdoor air pollutants. This work may bring inspirations for formulating local air pollutant standards and social policy regarding cardiovascular health of residents.