An integrated approach for conducting long-term PM2 5 exposure and health risk assessment for residents

Abstract

SUMMARY In this study the relationship of PM2 5 data sets obtained from the mobile monitoring station (MMS) stationary monitoring station (SMS) and Air Quality monitoring station of the Environmental Protection Agency monitoring station (AQMS) were established in order to describe the spatial and temporal variations of PM2 5 of the Shalu area and to build a long term databank for conducting exposure and health risk assessment for residents’ exposures to PM2 5 A stationary PM2 5 monitoring station was built next to EPA monitoring station In addition a mobile monitoring station was used to measure PM2 5 of the area simultaneously in 2013-2014 Samplings were performed during both the daytime and nighttime on both weekdays and weekends for one month per season Results show that most of daytime air pollution levels were significant higher than that of the nighttime due to the higher traffic flow and traffic density of the former Comparing the results between Mm and Sm indicating that using the data of AQMS might cause underestimation for assessing residents’ exposures Exposure assessment results show that annual mean value of SMS MMS and AQMS are 29 14 27 15 and 20 05 μg/m3 respectively which is exceed PM2 5 air quality annual standard (15μg/m3) regulated by the EPA High coefficient of determinations (R2) were found between AQMS and SMS and between SMS and MMS and hence an exposure databank of residents characterized with both spatial and temporal variations was established The obtained long term exposure profile of residents and the estimated incremental risks (IR) of the lung cancer cardiovascular disease and asthma are found to be unacceptable which urges the needs for identifying main PM2 5 pollution sources for initiating proper control strategies in the future Key words: Stationary measurement Mobile measurement PM2 5 Health risk assessment ? INTRODUCTION To date Environmental Protection Agency monitoring station measurements (AQMS) are widely used for characterizing air quality data but simply using AQMS could be inadequate to characterize residents’ exposures of the specific area Our study analyzes the correlation of PM2 5 data sets of the mobile measurements (MMS) stationary measurements (SMS) and AQMS in order to describe the spatial and temporal variations of PM2 5 in the area and to build a long term databank for conducting exposure and health risk assessment for residents’ exposures to PM2 5 MATTERALS AND METHODS The Shalu area was chosen as the target area A stationary PM2 5 monitoring station was built next to EPA monitoring station In addition a mobile monitoring station was used to measure PM2 5 of the area simultaneously in 2013-2014 Samplings were performed during daytime (7:00-10:00 AM) and nighttime (18:00-21:00 PM) on both weekdays and weekends for one month per season After eliminated high leverage value and outliers the correlations of MMS SMS and AQMS were established and spatial and temporal variations of PM2 5 in the area were assessed and finally a long term PM2 5 databank was constructed The Bayesian decision analysis (BDA) were used for conducting long term exposure and health risk assessment of residents by comparing with EPA PM2 5 air quality standards (STD24hr) ? RESULTS AND DISCUSSION Results show that most of daytime air pollution indicators were significant higher than that of the nighttime due to the higher traffic flow and traffic density for the former Comparing the results between MMS and SMS the former are higher than that of the latter mainly due to their monitoring site is closer to local emission sources Therefore using the data of AQMS might cause underestimation for assessing residents’ exposures Moreover high concentrations were found in winter which may be affected by its intrinsic unfavorable atmospheric dispersion Exposure assessment results show that annual mean value of SMS MMS and AQMS are 29 14 27 15 and 20 05 μg/m3 respectively which is exceed PM2 5 air quality annual standard (15 μg/m3) regulated by the EPA The coefficient of determination (R2) between AQMS and SMS are found to be 62 0% in spring 75 6% in summer 61 8% in fall and 85 6% in winter The R2 between SMS and MMS are 50 2% in spring 64 3% in summer 65 2% in fall and 73 0% in winter The above results suggest the possibility for effectively building an exposure databank of residents characterized with both spatial and temporal variations by combining the data of AQMS MMS and SMS Long term exposure profile of residents at Shalu area obtained by the BDA shows that residents' exposure rating (ER) most probability (i e 74%) falls to ER2 (i e 2 5 to 5 STD24hr) Using the same data sets the increment risk (IR) of lung cancer (46 2%) falls to ER4 (i e ?5*10-4) cardiovascular disease most probability (63 1%) falls to ER2 (i e 5*10-4 to 1 25*10-3) and asthma most probability (64 3%) falling to ER4 (i e 1 25*10-3? 2 5*10-3) ? CONCLUSION Our results suggest that simply using the data of AQMS might cause underestimation for assessing residents’ exposures Judging from the obtained R2 between AQMS and SMS and that obtained between SMS and MMS the present study suggests the possibility for effectively building an exposure databank of residents characterized with both spatial and temporal variations by combining the data of AQMS MMS and SMS The obtained long term exposure profile of residents and the estimated IR of the lung cancer cardiovascular disease and asthma are found to be unacceptable which urges the needs for identifying main PM2 5 pollution sources for initiating proper control strategies in the future

Date of Award	2016 Aug 30
Original language	English
Supervisor	Perng-Jy Tsai (Supervisor)