| Autor: |
Che W; Department of Civil and Environmental Engineering , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China.; HKUST Jockey Club Institute for Advanced Study , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China.; Institute for Environment and Climate Research , Jinan University , Guangzhou , China., Frey HC; Division of Environment and Sustainability , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China.; Department of Civil, Construction and Environmental Engineering , North Carolina State University , Campus Box 7908, Raleigh , North Carolina 27695-7908 , United States., Li Z; Division of Environment and Sustainability , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China., Lao X; JC School of Public Health and Primary Care , The Chinese University of Hong Kong , Hong Kong SAR , China., Lau AKH; Department of Civil and Environmental Engineering , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China.; Division of Environment and Sustainability , The Hong Kong University of Science and Technology , Clear Water Bay , Hong Kong , China. |
| Abstrakt: |
Ambient PM 2.5 concentrations measured at fixed site monitors (FSM) are often biased with respect to exposure concentrations because of spatial variability and infiltration. Based on comparison of ambient concentrations from 14 FSMs and of exposure concentrations measured indoors and outdoors at two schools in Hong Kong for winter and summer seasons, the magnitude and sources of exposure error based on using FSMs as a surrogate for exposure are quantified. An approach for bias correcting surrogate exposure estimates from FSMs is demonstrated. The approach is based on a proximity factor (PF) that accounts for differences in spatial locations, proximity to emissions and deviation from dominant wind direction, and an infiltration factor (IF) that varies by season. The combination of the PF and IF reduce bias in mean school exposure estimates from ±90% to ±20%. Bias in exposure estimates from using FSMs as surrogates tend to be smaller for which the exposure site and FSM are aligned with wind direction, have similar sampling height, and are in close proximity. The methodology demonstrated to assess concordance between FSMs and exposure measurement sites can be applied more broadly to help reduce exposure error, which may help to interpret seasonal variations in health estimates. |
