Exploring the background features of acidic and basic air pollutants around an industrial complex using data mining approach

Air pollution data around a monitored site are normally difficult to analyze due to highly inter-related meteorological and topographical factors on top of many complicated atmospheric chemical interactions occurred in local and regional wind fields. The challenge prompts this study to develop a comprehensive data-mining algorithm of cluster analysis followed by meteorological and interspecies correlations to mitigate the inherent data complexity and dissimilarity. This study investigated the background features of acidic and basic air pollutants around a high-tech industrial park in Taiwan. Monthly samplings were taken at 10 sites around the park in a year. The temporal distribution plots show a baseline with two characteristic groups of high and low peaks. Hierarchical cluster analysis confirms that high peaks were primarily associated with low speed south wind in summer for all the chemical species, except for F−, Cl−, NH3 and HF. Crosschecking with the topographical map identifies several major external sources in south and southwest. Further meteorological correlation suggests that HCl is highly positively associated with humidity, while Cl− is highly negatively associated with temperature, both for most stations. Interestingly, HNO3 is highly negatively associated with wind speed for most stations and the hotspot was found in summer and around the foothill of Da-Tu Mountain in the northwest, a stagnant pocket on the study site. However, F− is highly positively associated with wind speed at downwind stations to the prevailing north wind in winter, indicating an internal source from the north. The presence of NH4+ stimulates the formation of NO3-, SO4-2 (R = 0.7), and HNO3, H2SO4, NH3 (R = 0.3–0.4). As H2SO4 could be elevated to a level as high as 40% of the regulated standard, species interactions may be a dominate mechanism responsible for the substantial increase in summer from external sources.