dc.description.abstract | Fine suspended particles (PM2.5) mass and chemical components can be used to assess air quality, human health risks, and control effectiveness of pollution sources. Owing to measurement deviation between manual and automated methods in measuring PM2.5 mass concentration, this study used BGI PQ 200 (hereinafter referred to as PQ 200) and Thermo R & P 1405-F FDMS (hereinafter referred to as FDMS ) to measure PM2.5 mass concentration and used the R & P 2300 to measure PM2.5 chemical composition at three air quality monitoring stations (Xinzhuang, Lunbei, and Cianjhen) of Taiwan Environmental Protection Administration from December 17, 2012 to August 19, 2013. To further investigate the effects of sampling artifact corrections in different PM2.5 chemical speciation samplers, this study added Met One Super SASS (hereinafter referred to as Super SASS) and URG 3000N to measure PM2.5 components from May 20 to June 30, 2013. For source apportionment of PM2.5, this study adopted Positive Matrix Factorization (PMF) and validated the results by using Conditional Probability Function (CPF) coupling with high source contributions and the associated wind directions.
The results showed that the measured PM2.5 concentration difference between PQ 200 and FDMS (hereinafter referred to as FDMS-PQ 200) and PM2.5 concentration correlated well at both the Xinzhuang and Cianjhen stations. In contrast, (FDMS-PQ 200) only correlated weakly with atmospheric temperature at the Lunbei station. Further investigation showed that (FDMS-PQ 200) correlated moderately well with FDMS Reference MC at both the Xinzhuang and Cianjhen stations but not for the Lunbei station. Considering different temperatures and humic conditions operated in PQ 200 and FDMS, the difference between PQ 200 and FDMS Base MC was considered related to water content deviation of PM2.5 between the two methods at the Lunbei station. In contrast, the difference between PQ 200 and FDMS Base MC was accounted for by the retained semi-volatile ions of PM2.5 in PQ 200 at the Cianjhen station.
Using R&P 2300 and Super SASS with different sampling configurations to collect PM2.5 mass concentration showed no significant difference. The installation of preceding denuders can avoid from the interference of acidic and basic gases on the following deposited particles. Therefore, the concentrations of semi-volatile species of water-soluble ions such as NH4+ and NO3- on the first Teflon filter and the second Nylon filter of the Super SASS without preceding denuders were the highest among different configurations. Nylon filter can adsorb volatilized gases from deposited particles. The measured Cl- was thus the highest from the first Nylon filter and the lowest from the second Nylon filter in SASS 2N accordingly. Similarly, the measured volatilized NO3- from the second Nylon filter of SASS 2N was the lowest although the volatilized NO3- from the first Nylon filter was not the highest. For the part of carbonaceous content, the measured PM2.5 organic carbon (OC) from the first filter was the highest for Super SASS followed by R&P 2300 and URG 3000N, while EC showed no difference. This was influenced by the filter face velocity (URG 3000N> R&P 2300>Super SASS) as high face velocity will reduce the adsorption of volatile organic compounds (VOCs) from the atmosphere. It is noted that Super SASS estimates the adsorbed VOCs using passive field blank, which will underestimate positive artefacts of filter, overestimate volatilized OC, and lead to overestimation for PM2.5 OC correction.
The dominant PM2.5 component was SO42- in the first two seasons followed by the corrected OC in the last two seasons at the Xinzhuang station. SO42- was dominated in the first, second, and fourth seasons except that NO3- was the highest in the third season at the Lunbei station. Similarly, the most significant species was SO42- in the first, second, and fourth seasons, while the corrected OC was dominant in the third season at the Cianjhen monitoring station. Source contributions were conducted by using PMF with the aid of CPF to validate the results at the Xinzhuang, Lunbei, and Cianjhen stations. The most significant source types of PM2.5 were secondary sulfate and gasoline emissions at the Xinzhuang station. Secondary nitrate mixed with sea salt and biomass burning were the two most important source types at the Lunbei station. For the Cianjhen station, the results indicated that secondary nitrate and secondary sulfate were the two greatest contribution source types. PMF modeling required a data set of more than 100 data points, a smaller data set was found workable but was limited for clearer identification in distinguishing PM2.5 chemical species from different source profiles based on the comparison between 40 and 103 data points for the Xinzhuang station.
In summary, the difference in measuring PM2.5 mass concentrations between manual collection and automated methods is affected by the volatilization loss of semi-volatile particulate matter and moisture content of the collected particles. The FDMS tends to overestimate PM2.5 semi-volatile concentrations in cold season. This will lead to overestimate PM2.5 mass concentration when using FDMS in a place with great variations in the atmospheric temperature and relative humidity such as Taiwan. The speciation sampler without the installation of preceding denuders will collect greater concentrations of semi-volatile species of water-soluble ions. PMF modeling results show that secondary sulfate, secondary nitrate, and gasoline emissions significantly contribute to PM2.5 concentrations in Taiwan. Reducing precursor source contributions of these source types will improve PM2.5 air quality. | en_US |