本溪市大气主要污染物与气象因素相关性

目的分析本溪市2014—2015年大气主要污染物与气象因素的相关性,为大气污染防治提供依据。方法本溪市环境监测站共设立6个大气监测点(溪湖、彩屯、东明、大峪、新立屯和威宁)进行常年大气污染物监测工作。选取2014—2015年大气二氧化硫(SO2)、二氧化氮(NO_2)、可吸入颗粒物(PM_(10))和细颗粒物(PM_(2.5))日均浓度与气象监测资料进行相关性分析和多元逐步回归分析,找出气象因素与大气污染物浓度的关系及气象因素对大气污染物浓度的影响规律。结果 SO_2日均浓度与气温和相对湿度呈负相关(r=–0.793、–0.288,P均0.01);PM_(10)与气温、风速、湿相对度均呈负相关(r=–0.338、–0.176、–0.138,P均0.01);NO_2与温度和风速呈负相关(r=–0.507、–0.313,P均0.01);PM_(2.5)与温度和风速呈负相关(r=–0.379、–0.264,P均0.01)。结论气象因素与大气污染物浓度密切相关,气象因素对大气污染物浓度的影响有一定规律性,可通过回归方程进行模拟预测。     

基于典型相关分析的潍坊市大气污染物与气象因素关系的初步探讨

为探讨潍坊市大气污染物与气象因素之间的关系,收集该市大气污染物和气象因素监测数据,采用简单相关和典型相关分析探讨二者关系。简单相关分析结果显示,日均气温、日最高气温、日最低气温、气温日较差与大气污染物PM_(2.5)、PM_(10)、CO、NO_2、SO_2相关,其中气温日较差与PM_(2.5)、PM_(10)、CO、NO_2、SO_2呈正相关,日均气温、日最高气温、日最低气温与PM_(2.5)、PM_(10)、CO、NO_2、SO_2呈负相关,均有统计学意义(P0.05);日均相对湿度与PM_(2.5)呈正相关,与PM_(10)、NO_2、SO_2呈负相关;日均风速与PM_(2.5)、PM_(10)、CO、NO_2、SO_2呈负相关,日均降水量与PM_(2.5)、PM_(10)、CO、NO_2、SO_2呈负相关,均有统计学意义(P0.05)。典型相关分析共提取了4对典型相关变量,典型相关系数分别为0.78、0.74、0.48和0.34。提示研究期间潍坊市气温主要影响气态污染物浓度,相对湿度主要影响PM_(2.5)浓度,风速主要对PM_(10)和NO_2浓度产生较大影响,日均降水量和气温日较差主要影响大气颗粒物浓度。     

2014-2018年南昌市大气PM2.5对儿童呼吸系统疾病门诊量的影响

目的分析大气PM_(2.5)对南昌市儿童呼吸系统疾病日门诊量的影响。方法收集2014—2018年南昌市大气污染物、气象、儿童呼吸系统门诊量资料。采用基于Poisson回归的广义线性模型,控制长期和季节变化趋势、气象因素、星期几效应等因素,分析大气PM_(2.5)对儿童呼吸系统门诊量的影响。结果 2014—2018年南昌市PM_(2.5)逐年平均浓度为51、42、43、42、30μg/m~3。空气质量为良、轻度污染、中度污染、重度污染天气的儿童呼吸系统疾病日门诊量均高于空气质量为优的天气,且差异均具有统计学意义(P0.05),门诊量增幅分别为8.94%、14.95%、18.30%、11.78%。单污染物模型显示,PM_(2.5)在当日效应最强,浓度每升高10μg/m~3,儿童呼吸系统疾病门诊量增加0.19%(95%CI:0.12%～0.26%);累积滞后0～7 d的儿童呼吸系统疾病门诊量增加0.25%(95%CI:0.14%～0.36%)。多污染物模型显示,在引入O_(3-8h)后,PM_(2.5)浓度每增加10μg/m~3,当日儿童呼吸系统疾病门诊量增加0.15%(95%CI:0.09%～0.22%)。结论 2014—2018年南昌市大气PM_(2.5)浓度升高会引起使儿童呼吸系统疾病门诊量增加。     

贵阳市大气颗粒物污染特征分析

利用贵阳市南明区和花溪区2018年1～12月PM_(10)、PM_(2.5)、NO_2、CO质量浓度分析大气颗粒物污染特征,结果表明:两区PM_(10)、PM_(2.5)污染物都在单日上有超过国家空气质量二级标准,PM_(10)、NO_2、PM_(2.5)、CO污染物年平均浓度值花溪区均低于南明区,且两区平均浓度间差异有统计学意义(P0.05),南明区SO_2年平均浓度与花溪区相比差异无统计学意义(P0.05)。     

兰州市医院儿科呼吸系统疾病日门诊量与空气污染的相关性研究

目的探讨兰州市医院儿科呼吸系统疾病日门诊量与空气污染的关系。方法收集2014年、2015年兰州市城关区和西固区监测点覆盖范围内的综合医院、社区卫生服务中心及妇幼保健院等6家医疗机构的儿科呼吸系统疾病门诊量数据及兰州市空气污染物资料,采用两独立样本的秩和检验分析两城区空气质量及两城区与兰州市总体空气质量的比较。采用Poisson广义可加模型的时间序列分析,对兰州市医院儿科呼吸系统疾病日门诊量和空气污染进行分析,同时控制时间趋势、星期效应、气象因素等混杂因素的影响。结果兰州市城关区和西固区SO2日平均浓度分别为26.52和28.78μg/m~3,NO2日平均浓度分别为48.56和51.84μg/m~3,PM10日平均浓度分别为115.82和129.31μg/m~3,PM_(2.5)日平均浓度分别为51.34和62.86μg/m~3。两城区空气污染物的秩和检验结果显示:西固区SO_2、PM_(10)、PM_(2.5)的日平均浓度高于城关区此三种污染物的日平均浓度,且差异有统计学意义。城关区和西固区的空气污染物浓度比兰州市总体的浓度高。广义可加模型分析结果发现SO_2、NO_2、PM_(10)及PM_(2.5)日平均浓度与儿科呼吸系统疾病日门诊量存在正相关关系。进行多污染物模型分析发现,多污染物模型的RR值相对单污染物模型基本没有升降。结论兰州市医院儿科呼吸系统疾病日门诊量与空气污染浓度呈正相关关系,且存在滞后效应。     

西安市空气质量与气象因素的典型相关分析

目的通过对2013年11月1日—2014年10月31日西安市空气污染指标,以及气象因素指标的分析,揭示西安市气象因素对空气污染物浓度的影响规律。方法对研究期间AQI指数、PM_(2.5)、PM_(10)、SO_2、NO_2、CO空气污染物水平进行统计描述;采用简单相关和典型相关分析,探讨空气主要污染物与气温、气湿、风速、气压、降雨量等气象因素之间的关系。结果根据《环境空气质量标准》(GB 3095-2012)年均值二级标准,研究期间,(1)西安市PM_(2.5)和PM_(10)年均值均超标,CO和SO_2达到二级标准,而NO_2略超过二级标准;(2)西安市气压变化平稳,月均湿度在70%上下波动,秋季略高,风速全年较平稳,冬春季略低,降雨量表现为冬春季偏低,夏季略高,秋季明显增多;(3)简单相关分析表明,气温同AQI、PM_(2.5)、PM_(10)、CO、NO_2和SO_2均有显著的相关关系,相关系数均大于0.5;气湿与SO_2的相关系数较大;风速与NO_2的相关系数较大,接近0.5;气压与CO、NO_2和SO_2的相关系数较大,均大于0.5;降雨量与AQI、PM_(2.5)、PM_(10)、CO、NO_2和SO_2均有显著的相关关系,相关系数较小。(4)典型相关分析表明,气象因素中气温主要影响气态污染物的浓度,湿度主要影响PM_(2.5)的浓度,而风速主要对NO_2浓度产生较大影响,降雨量则主要影响的是颗粒态污染物的浓度。结论在本研究期内,西安市空气质量与气象因素间有相关性。     

长沙市城区大气PM_(2.5)浓度与居民每日死亡关系研究

[目的]探讨长沙市城区大气污染物PM_(2.5)暴露对居民每日死亡风险的影响。[方法]收集2014年1月1日至2016年12月31日期间长沙市城区每日温度、相对湿度等气象数据,PM_(2.5)、PM_(10)、NO_2、SO_2、CO等大气污染物数据和居民每日死亡数据。采用分布滞后非线性模型,控制时间长期趋势、气象因素、星期几及节假日效应等混杂因素,分析PM_(2.5)单独暴露及其与PM_(10)、NO_2、SO_2、CO等联合暴露当日至滞后14 d时居民每日总死亡、心血管疾病死亡和呼吸系统疾病死亡的风险。[结果]长沙市城区PM_(2.5)年均质量浓度(以下简称"浓度")为63μg/m3。单污染物模型显示,PM_(2.5)质量浓度上升10μg/m3时,致居民每日总死亡(lag10)和每日心血管疾病死亡(lag1)的风险(RR及其95%CI)分别为1.051 8(1.006 5~1.099 4)和1.086 1(1.005 6~1.173 0),对居民呼吸系统疾病死亡的影响无统计学意义。双污染物模型分析显示,分别引入NO_2、SO_2后,PM_(2.5)致居民每日总死亡的风险增加(RR=1.084 3,95%CI:1.027 8~1.143 9;RR=1.067 9,95%CI:1.015 5~1.123 0),致每日心血管疾病死亡、呼吸系统疾病死亡的风险降低;引入CO后,PM_(2.5)致居民每日总死亡、每日心血管疾病死亡的风险增加,致每日呼吸系统疾病死亡的风险降低。[结论]长沙市城区PM_(2.5)浓度升高可导致居民总死亡的风险增加。     

济宁市冬春季大气PM_(2.5)与气象因素相关性研究

目的探讨济宁市2014年1—3月大气PM_(2.5)污染与气象因素的相关性,探讨大气PM_(2.5)浓度变化原因,为大气PM_(2.5)的监测、预警和污染防治提供参考。方法收集济宁市电化厂、火炬城、监测站3个大气自动监测点自2014年1月1日至3月31日的大气PM_(2.5)日均浓度数据,及中国科学数据共享服务网的济宁市地面气象资料数据,并进行相关分析。结果济宁市1月大气PM_(2.5)日均浓度高于2、3月,差异有统计学意义(P0.05),但同时期3个监测点之间的浓度差异无统计学意义(P=0.767)。大气PM_(2.5)日均浓度与相对湿度呈正相关,与能见度、风速呈负相关,其中与能见度的相关性最高。经多元线性逐步回归分析,影响大气PM_(2.5)日均浓度的主要气象因素为能见度、降水量和相对湿度(回归方程:yPM_(2.5)平均浓度=142.658-9.831x能见度-29.436x降水量+0.622x相对湿度,F=37.345,P0.01)。结论气象因素对大气PM_(2.5)有一定影响,其中能见度、降水量和相对湿度对PM_(2.5)日均浓度影响较明显。     

沈阳市铁西区大气PM2.5对冠心病住院的影响

目的探讨沈阳市铁西区大气PM_(2.5)对居民冠心病住院的影响,为大气PM_(2.5)健康效应研究提供依据。方法收集2017年1月1日—2017年12月31日沈阳市铁西区大气污染物浓度、气象因素及沈阳医学院附属中心医院冠心病住院患者的相关临床数据,采用1∶2分层病例交叉方法,利用条件logistic回归探讨大气PM_(2.5)对居民冠心病住院的影响。结果研究期间沈阳市该区大气PM_(2.5)日均浓度为53.47μg/m~3,有77 d超过《环境空气质量标准》(GB 3095—2012)规定的二级浓度限值,超标率为21.10%。研究期间共收集4 148例冠心病住院患者资料,日均住院11例。调整气象因素的影响后,单污染物模型显示,PM_(2.5)浓度每增加10μg/m~3对冠心病住院的影响在当日达到最大,OR值为1.020(95%CI:1.006～1.033),结果有统计学意义(P0.05)。进一步将CO、NO_2、SO_2、O_3纳入模型,分别构建双污染物、三污染物、四污染物、五污染物模型,结果显示,PM_(2.5)浓度每增加10μg/m~3对居民冠心病住院影响的OR值均有统计学意义(P0.05)。分层分析结果显示,大气PM_(2.5)浓度每增加10μg/m~3,男性居民冠心病住院风险增加,OR值为1.026(95%CI:1.005～1.046);年龄小于60岁居民冠心病住院风险增加,OR值为1.029(95%CI:1.007～1.052);不吸烟居民冠心病住院风险增加,OR值为1.022(95%CI:1.007～1.037);处于采暖期的居民冠心病住院风险增加,OR值为1.031(95%CI:1.015～1.046);上述结果均有统计学意义(P0.05)。结论大气PM_(2.5)浓度升高可能导致居民冠心病住院风险增加,控制大气PM_(2.5)污染对降低人群冠心病住院风险具有重要意义。     

淮安市空气PM_(2.5)污染对呼吸系统疾病门诊量影响

目的了解淮安市空气PM_(2.5)污染对人群呼吸系统疾病门诊量影响。方法通过医保信息系统,收集淮安市城区人群2013-2014年呼吸系统逐日发病数据,结合同期大气污染监测数据和气象资料,运用统计学方法分析PM_(2.5)污染水平及其对呼吸系统疾病日门诊量影响。结果2013-2014年淮安市空气PM_(2.5)质量浓度均值为73.7μg/m3,超标252d(占34.5%)。PM_(2.5)质量浓度与居民呼吸系统疾病日门诊量存在正相关关系且有滞后性,滞后效应以第4d最强,PM_(2.5)质量浓度每增加10μg/m3呼吸系统疾病日门诊量增加0.63%(95%CI:0.37%~0.89%)。结论淮安市空气PM_(2.5)污染较严重,能增加呼吸系统疾病门诊量,建议继续加强空气质量监测,减少大气污染物排放,保护居民健康。     

山西省主要恶性肿瘤发病情况与大气主要污染物的相关性研究

目的 探讨环境中大气主要污染物与山西省主要恶性肿瘤发生的相关性.方法 收集2014年1-12月山西省肿瘤医院诊治的10种主要恶性肿瘤患者资料,以及各月大气中空气动力学直径＜2.5 μm的细颗粒物(PM2.5)、空气动力学直径在2.5～10 μm的粗颗粒(PM10)、二氧化硫(SO2)、二氧化氮(NO2)、一氧化碳(CO)、臭氧(O3)浓度和空气质量指数(AQI).运用SPSS 17.0和SAS 9.2进行相关分析及回归分析曲线拟合.结果 恶性肿瘤发病与PM2.5 (r=0.55)、PM10(r=0.49)、SO2(r=0.56)、O3(r=0.56)均显著相关(P均＜0.05),与AQI呈负相关(r=-0.65,P＜0.05).肺癌与PM2.5 (r=0.54)呈正相关,宫颈癌和肠癌与PM25(r=0.55、0.61)、PM10(r=0.61、0.57)、SO2(r=0.68、0.59)、O3(r=0.65、0.59)均呈正相关,胃癌与PM2.5(r=0.54)、PM10(r=0.52)、SO2(r=0.52)呈正相关,淋巴癌与PM2.5(r=0.57)、SO2(r=0.74)、O3(r=0.54)呈正相关,肝癌与PM2.5 (r=0.62)、PM10(r=0.59)呈正相关,卵巢癌与PM10(r=0.64)呈正相关,乳腺癌和食管癌与O3 (r=0.71、0.53)呈正相关.AQI除了与乳腺癌和膀胱癌未表现出显著相关性,与其他肿瘤均呈现出显著负相关.恶性肿瘤发病与AQI呈二次曲线相关.结论 大气污染可能会导致恶性肿瘤发病增加,对健康的潜在危害不容忽视.     

Characterization of PM2.5, gaseous pollutants, and meteorological interactions in the context of time-series health effects models

Associations of particulate matter (PM) and ozone with morbidity and mortality have been reported in many recent observational epidemiology studies. These studies often considered other gaseous co-pollutants also as potential confounders, including nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). However, because each of these air pollutants can have different seasonal patterns and chemical interactions, the estimation and interpretation of each pollutant's individual risk estimates may not be straightforward. Multi-collinearity among the air pollution and weather variables also leaves the possibility of confounding and over- or under-fitting of meteorological variables, thereby potentially influencing the health effect estimates for the various pollutants in differing ways. To investigate these issues, we examined the temporal relationships among air pollution and weather variables in the context of air pollution health effects models. We compiled daily data for PM less than 2.5 mum (PM2.5), ozone, NO2, SO2, CO, temperature, dew point, relative humidity, wind speed, and barometric pressure for New York City for the years 1999-2002. We conducted several sets of analyses to characterize air pollution and weather data interactions, to assess different aspects of these data issues: (1) spatial/temporal variation of PM2.5 and gaseous pollutants measured at multiple monitors; (2) temporal relationships among air pollution and weather variables; and (3) extent and nature of multi-collinearity of air pollution and weather variables in the context of health effects models. The air pollution variables showed a varying extent of intercorrelations with each other and with weather variables, and these correlations also varied across seasons. For example, NO2 exhibited the strongest negative correlation with wind speed among the pollutants considered, while ozone's correlation with PM2.5 changed signs across the seasons (positive in summer and negative in winter). The extent of multi-collinearity problems also varied across pollutants and choice of health effects models commonly used in the literature. These results indicate that the health effects regression need to be run by season for some pollutants to provide the most meaningful results. We also find that model choice and interpretation needs to take into consideration the varying pollutant concurvities with the model co-variables in each pollutant's health effects model specification. Finally, we provide an example for analysis of associations between these air pollutants and asthma emergency department visits in New York City, which evaluate the relationship between the various pollutants' risk estimates and their respective concurvities, and discuss the limitations that these results imply about the interpretability of multi-pollutant health effects models.     

北京一次空气重污染过程室内外微生物气溶胶的浓度变化及影响因素分析

目的 分析北京一次空气重污染黄色预警期间室内外微生物气溶胶的浓度和粒径变化特征及相关影响因素。方法 采用Andersen空气微生物采样器在北京市空气重污染黄色预警期间对室内外环境进行采样、培养,同时记录采样时的环境因素、颗粒物以及气态污染物的浓度。结果 在本次北京市空气重污染期间室外细菌和真菌气溶胶浓度显著高于室内细菌和真菌气溶胶浓度(P<0.01),室内外细菌和真菌浓度变化趋势具有显著正相关(P<0.01),发现63.62%~96.70%的细菌或真菌气溶胶粒子直径小于5μm,Spearman相关分析表明室外细菌气溶胶浓度与温度具有显著正相关(P<0.01),与相对湿度具有显著负相关(P<0.01),室内细菌气溶胶浓度与温度和相对湿度具有显著正相关(P<0.01),室外真菌气溶胶浓度与SO2、PM10、PM2.5和AQI指数具有显著正相关(P<0.01),与相对湿度具有显著负相关,室内真菌气溶胶浓度与温度、SO2、PM10、PM2.5和AQI指数具有显著正相关(P<0.01),与O3浓度具有显著负相关(P<0.01)。结论 本次空气重污染预警期间,室外微生物气溶胶浓度显著高于室内,超过60%的室外或室内微生物气溶胶粒子直径小于5μm,室内外微生物气溶胶浓度受多个环境因素参数影响。     

Associations between air pollution and mortality in Phoenix, 1995-1997

We evaluated the association between mortality outcomes in elderly individuals and particulate matter (PM) of varying aerodynamic diameters (in micrometers) [PM(10), PM(2.5), and PM(CF )(PM(10) minus PM(2.5))], and selected particulate and gaseous phase pollutants in Phoenix, Arizona, using 3 years of daily data (1995-1997). Although source apportionment and epidemiologic methods have been previously combined to investigate the effects of air pollution on mortality, this is the first study to use detailed PM composition data in a time-series analysis of mortality. Phoenix is in the arid Southwest and has approximately 1 million residents (9. 7% of the residents are > 65 years of age). PM data were obtained from the U.S. Environmental Protection Agency (EPA) National Exposure Research Laboratory Platform in central Phoenix. We obtained gaseous pollutant data, specifically carbon monoxide, nitrogen dioxide, ozone, and sulfur dioxide data, from the EPA Aerometric Information Retrieval System Database. We used Poisson regression analysis to evaluate the associations between air pollution and nonaccidental mortality and cardiovascular mortality. Total mortality was significantly associated with CO and NO(2) (p < 0.05) and weakly associated with SO(2), PM(10), and PM(CF) (p < 0. 10). Cardiovascular mortality was significantly associated with CO, NO(2), SO(2), PM(2.5), PM(10), PM(CF) (p < 0.05), and elemental carbon. Factor analysis revealed that both combustion-related pollutants and secondary aerosols (sulfates) were associated with cardiovascular mortality.     

Indoor air quality in ice skating rinks in Hong Kong

Indoor air quality in ice skating rinks has become a public concern due to the use of propane- or gasoline-powered ice resurfacers and edgers. In this study, the indoor air quality in three ice rinks with different volumes and resurfacer power sources (propane and gasoline) was monitored during usual operating hours. The measurements included continuous recording of carbon monoxide (CO), carbon dioxide (CO(2)), total volatile organic compounds (TVOC), particulate matter with a diameter less than 2.5 microm (PM(2.5)), particulate matter with diameter less than 10 microm (PM(10)), nitric oxide (NO), nitrogen dioxide (NO(2)), nitrogen oxide (NO(x)), and sulfur dioxide (SO(2)). The average CO, CO(2), and TVOC concentrations ranged from 3190 to 6749 microg/m(3), 851 to 1329 ppm, and 550 to 765 microg/m(3), respectively. The average NO and NO(2) concentrations ranged from 69 to 1006 microg/m(3) and 58 to 242 microg/m(3), respectively. The highest CO and TVOC levels were observed in the ice rink which a gasoline-fueled resurfacer was used. The highest NO and NO(2) levels were recorded in the ice rink with propane-fueled ice resurfacers. The air quality parameters of PM(2.5), PM(10), and SO(2) were fully acceptable in these ice rinks according to HKIAQO standards. Overall, ice resurfacers with combustion engines cause indoor air pollution in ice rinks in Hong Kong. This conclusion is similar to those of previous studies in Europe and North America.     

Ischemic heart disease and ambient air pollution of particulate matter 2.5 in 51 counties in the U.S

OBJECTIVE: Ischemic heart disease (IHD) is one of the most common health threats to the adult population of the U.S. and other countries. The objective of this study was to examine the association between exposure to elevated annual average levels of Particulate matter 2.5 (PM2.5) air quality index (AQI) and IHD in the general population. METHODS: We combined data from the Behavioral Risk Factor Surveillance System and the U.S Environmental Protection Agency air quality database. We analyzed the data using SUDAAN software to adjust the effects of sampling bias, weights, and design effects. RESULTS: The prevalence of IHD was 9.6% among respondents who were exposed to an annual average level of PM2.5 AQI > 60 compared with 5.9% among respondents exposed to an annual average PM2.5 AQI < or = 60. The respondents with higher levels of PM2.5 AQI exposure were more likely to have IHD (adjusted odds ratio = 1.72, 95% confidence interval 1.11, 2.66) than respondents with lower levels of exposure after adjusting for age, gender, race/ethnicity, education, smoking, body mass index, diabetes, hypertension, and hypercholesterolemia. CONCLUSIONS: Our study suggested that exposure to relatively higher levels of average annual PM2.5 AQI may increase the likelihood of IHD. In addition to encouraging health-related behavioral changes to reduce IHD, efforts should also focus on implementing appropriate measures to reduce exposure to unhealthy AQI levels.     

Effects of particulate and gaseous air pollution on cardiorespiratory hospitalizations

We obtained data on daily numbers of admissions to hospital in Toronto, Canada, from 1980 to 1994 for respiratory, cardiac, cerebral vascular, and peripheral vascular diseases. We then linked the data to daily measures of particulate mass less than 10 microns in aerodynamic diameter (PM10), particulate mass less than 2.5 microns in aerodynamic diameter (PM2.5), and particulate mass between 2.5 and 10 microns in aerodynamic diameter (PM10-2.5), ozone, carbon monoxide, nitrogen dioxide, and sulfur dioxide. Air pollution was only associated weakly with hospitalization for cerebral vascular and peripheral vascular diseases. We controlled for temporal trends and climatic factors, and we found that increases of 10 microg/m3 in PM10, PM2.5, and PM10-2.5 were associated with 1.9%, 3.3%, and 2.9% respective increase in respiratory and cardiac hospital admissions. We further controlled for gaseous pollutants, and the percentages were reduced to 0.50%, 0.75%, and 0.77%, respectively. Of the 7.72 excess daily hospital admissions in Toronto attributable to the atmospheric pollution mix, 11.8% resulted from PM2.5, 8.2% to PM10-2.5, 17% to carbon monoxide, 40.4% to nitrogen dioxide, 2.8% to sulfur dioxide, and 19.8% to ozone.     

2015—2018年西安市两城区PM2.5质量浓度变化特征及气象影响因素

目的了解西安市莲湖区和雁塔区PM2.5质量浓度的变化特征及其与气象条件的关系。方法 2015-2018年,根据2012年西安市6个主城区全部环保站点的环保监测数据,包括NO2、SO2,PM10、PM2.5、CO和O3,选择上述污染物浓度相对较高的莲湖区和相对较低的雁塔区分别采集空气样本,按照国家环保部《环境空气PM10和PM2.5的测定重量法》(HJ 618-2011)开展PM2.5的质量浓度检测。依据《环境空气质量标准》(GB 3095-2012)中日均二级浓度限值标准(75μg/m3),按照不同年度、区域和季节对检测结果分别开展统计分析和评价。收集同期西安市气象局气象数据资料,包括日平均温度、日平均气压、日均相对湿度、日平均风速、日降水量、最高温度和最低温度,分析PM2.5质量浓度与气象影响因素的关系。结果共采集分析空气样本660份,PM2.5质量浓度中位数为71μg/m3,达标356份,样本总达标率为53.94%,4年样本达标率由高到低依次为2017年>2018年>2016年>2015年(P<0.001),全部样品PM2.5质量浓度平均水平由高到低依次为2015年>2016年>2017年>2018年(P<0.001)。样本达标率和PM2.5质量浓度在莲湖区、雁塔区间的差异均无统计学意义(P>0.05)。不同季节样品达标率由高到低依次为夏季>春季>秋季>冬季(P<0.05);不同季节PM2.5质量浓度平均水平由高到低依次为冬季>秋季>春季>夏季(P<0.001)。日均温度、日均气压、日均风速、日均相对湿度、降水量和最低温度同PM2.5质量浓度显著相关(P<0.001)。莲湖区和雁塔区气象因素多元回归分析的调整后R2分别为0.390和0.373。结论西安市两城区空气质量逐年改善,秋冬季PM2.5污染较为严重。气象因素影响大气中PM2.5质量浓度水平。     

Towards an improved air quality index

Air quality indices (AQI) are commonly used to indicate the level of severity of air pollution to the public. A number of methods were developed in the past by various researchers/environmental agencies for the calculation of AQI, but there is no universally accepted method, appropriate for all situations. An updated review of the major air quality indices developed worldwide is presented in this paper. These methods differentiate mainly in the number of pollutants included, its sampling period and air quality classes and breakpoints. When applying different AQI to a common case study, important differences are found in terms of the classification of the quality of the air. The purposes of this research are to identify weaknesses of the current AQI and to discuss possible changes and updates with Portugal as case study. A survey, with 10 questions about the calculation and use of the AQI and its dissemination to public, was delivered to the five regional environmental agencies in Portugal and, based on results, modifications to the current AQI are proposed. Two main changes—inclusion of PM2.5 and specific urban/industrial AQI—were tested, comparing the current and the proposed AQI along the 2014 year. It is observed that a significant difference exists when specific urban and industrial sites are considered when calculating the AQI. On the other hand, and contrarily to other regional studies, the results show that the inclusion of fine suspended particulate (PM2.5) does not impact the final AQI value.     

合肥市大气污染与肺炎门诊量的时间序列研究

目的探讨大气污染物SO_2、NO_2和PM_(2.5)浓度与合肥市滨湖医院肺炎日门诊量之间的关系。方法采用时间序列分析的广义相加Poisson回归模型,在控制长期趋势、星期几效应和气象因素等混杂因素的影响后,定量分析2014年安徽省合肥市大气污染物SO_2、NO_2、PM_(2.5)日均浓度与滨湖医院肺炎日门诊量的关系及滞后效应。结果单污染物模型中,在控制了长期趋势、星期几效应和气象因素的影响后,SO_2在滞后3、4、5 d(lag3、lag4、lag5)时对肺炎日门诊量的影响有统计学意义(P0.05),NO_2滞后2、3、4、5 d(lag2、lag3、lag4、lag5)时的影响有统计学意义(P0.01),PM_(2.5)滞后3、4 d(lag3、lag4)时的影响有统计学意义(P0.05);SO_2、NO_2、PM_(2.5)的滞后效应分别在lag3、lag2、lag4时最明显,当SO_2、NO_2、PM_(2.5)浓度每升高10μg/m~3时,肺炎日门诊量分别增加1.54%(95%CI:0.28%~2.81%),1.98%(95%CI:0.89%~3.08%)和0.28%(95%CI:0.06%~0.50%)。多污染物模型中,当模型中引入两种或两种以上的污染物后,各污染物对肺炎日门诊量的效应估计值均较单污染物模型降低,但并不改变各污染物与肺炎日门诊量之间的正向关联。结论合肥市大气污染物SO_2、NO_2、PM_(2.5)浓度升高可能引起医院肺炎日门诊量增加,且有一定的滞后效应。