Gurram Sashikanth, Stuart Amy Lynette, Pinjari Abdul Rawoof
Department of Civil and Environmental Engineering, University of South Florida, Tampa, USA.
Department of Environmental and Occupational Health, University of South Florida, 13201 Bruce B. Downs Blvd., MDC 56, Tampa, FL 33612 USA ; Department of Civil and Environmental Engineering, University of South Florida, Tampa, USA ; School of Population Health, University of Western Australia, Crawley, Australia.
Air Qual Atmos Health. 2015;8(1):97-114. doi: 10.1007/s11869-014-0275-6. Epub 2014 Jul 10.
Daily exposures to ambient oxides of nitrogen were estimated here for residents of Hillsborough County, FL. The 2009 National Household Travel Survey provided geocoded data on fixed activity locations during each person-day sampled. Routes between activity locations were calculated from transportation network data, assuming the quickest travel path. To estimate daily exposure concentrations for each person-day, the exposure locations were matched with diurnally and spatially varying ambient pollutant concentrations derived from CALPUFF dispersion model results. The social distribution of exposures was analyzed by comparing frequency distributions of grouped daily exposure concentrations and by regression modeling. To investigate exposure error, the activity-based exposure estimates were also compared with estimates derived using residence location alone. The mean daily activity-based exposure concentration for the study sample was 17 μg/m, with values for individual person-day records ranging from 7.0 to 43 μg/m. The highest mean exposure concentrations were found for the following groups: black (20 μg/m), below poverty (18 μg/m), and urban residence location (22 μg/m). Urban versus rural residence was associated with the largest increase in exposure concentration in the regression (8.3 μg/m). Time in nonresidential activities, including travel, was associated with an increase of 0.2 μg/m per hour. Time spent travelling and at nonresidential locations contributed an average of 6 and 24 %, respectively, to the daily estimate. A mean error of 3.6 %, with range from -64 to 58 %, was found to result from using residence location alone. Exposure error was highest for those who travel most, but lowest for the sociodemographic subgroups with higher mean exposure concentrations (including blacks and those from below poverty households). This work indicates the importance of urbanicity to social disparities in activity-based air pollution exposures. It also suggests that exposure error due to using residence location may be smaller for more exposed groups.
本文估算了佛罗里达州希尔斯伯勒县居民每日接触环境氮氧化物的情况。2009年全国家庭旅行调查提供了每个人采样日固定活动地点的地理编码数据。活动地点之间的路线是根据交通网络数据计算得出的,假设为最快的出行路径。为了估算每个人采样日的每日接触浓度,将接触地点与源自CALPUFF扩散模型结果的昼夜和空间变化的环境污染物浓度进行匹配。通过比较分组每日接触浓度的频率分布和回归建模来分析接触的社会分布情况。为了调查接触误差,还将基于活动的接触估算值与仅使用居住地点得出的估算值进行比较。研究样本基于活动的平均每日接触浓度为17μg/m³,个人采样日记录的值范围为7.0至43μg/m³。以下群体的平均接触浓度最高:黑人(20μg/m³)、贫困线以下人群(18μg/m³)和城市居住地点人群(22μg/m³)。在回归分析中,城市与农村居住情况与接触浓度的最大增幅相关(8.3μg/m³)。非居住活动(包括出行)的时间与每小时增加0.2μg/m³相关。出行时间和在非居住地点花费的时间分别对每日估算值平均贡献6%和24%。发现仅使用居住地点会导致平均误差为3.6%,范围从-64%至58%。出行最多的人接触误差最高,但平均接触浓度较高的社会人口亚组(包括黑人和贫困线以下家庭的人)接触误差最低。这项工作表明城市性对基于活动的空气污染接触中的社会差异具有重要意义。它还表明,对于接触更多的群体,使用居住地点导致的接触误差可能较小。