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

Area of Science:

• Environmental Science
• Atmospheric Chemistry
• Data Mining

Background:

• Analyzing air pollution data is challenging due to complex meteorological, topographical, and atmospheric chemical interactions.
• Understanding pollutant behavior around industrial sites is crucial for environmental management.

Purpose of the Study:

• To develop a data-mining algorithm combining cluster analysis and meteorological correlations to simplify air pollution data analysis.
• To investigate the background features and sources of acidic and basic air pollutants near a high-tech industrial park in Taiwan.

Main Methods:

• Conducted monthly air pollutant sampling at 10 sites over one year.
• Employed hierarchical cluster analysis to group pollutant behaviors.
• Performed meteorological and interspecies correlation analyses to identify source influences and chemical interactions.

Main Results:

• High pollutant peaks were associated with low-speed southerly winds in summer, except for specific species (F(-), Cl(-), NH(3), HF).
• External pollution sources were identified to the south and southwest, with internal sources indicated to the north in winter.
• Specific correlations found: HCl with humidity, Cl(-) with temperature, and HNO(3) with wind speed, highlighting a stagnant pocket near Da-Tu Mountain.
• Ammonium (NH(4)(+)) presence stimulated the formation of nitrate (NO(3)(-)) and sulfate (SO(4)(-2)), and acid formation (HNO(3), H(2)SO(4)).

Conclusions:

• The developed data-mining approach effectively mitigates complexity in air pollution data analysis.
• Meteorological conditions and interspecies chemical reactions significantly influence pollutant levels and distributions around industrial parks.
• Species interactions, particularly involving ammonium, play a dominant role in summer pollutant increases from external sources.