Use of Kolmogorov-Zurbenko Filter Technique to Evaluate PM2.5 Air Quality Trends in New Jersey
DOI:
https://doi.org/10.47611/jsrhs.v12i1.4393Keywords:
PM2.5, Kolmogorov-Zurbenko Filter, long-term trend analysis, Monthly trendsAbstract
This study examined the long-term trends of PM2.5 in the three counties, Camden, Bergen, and Middlesex in New Jersey between 2000 and 2022 using three different averaging methods: USEPA's 3-year average of highest annual average (TYAHAA), simple annual average, and KZ filter rolling average technique. As results, winter and summer had higher PM2.5 levels than spring and fall, which could be attributed to weather patterns and emission changes. For Camden County, an industrialized area, the KZ (-43%) and the TYAHAA (-41%) had a robust agreement. This suggests both KZ and the TYAHAA method could be used to evaluate long term trends for industrialized areas. As to Bergen County, an urban residential area with intensive transportation sources, the KZ (-29%) showed a disagreement with the TYAHAA (-41%) but correlated with the simple annual average (-25%). This suggests that the KZ is better at capturing transportation-related short-term fluctuations than the TYAHAA. However, the KZ average (+9%) had a significant discrepancy from the TYAHAA (-41%) for a suburban site. These discrepancies may be due to various factors, including differences in emission sources, meteorological conditions, emission control measures, and the time period analyzed. In conclusion, the KZ method can be useful for identifying and removing short-term fluctuations in data to reveal underlying long-term trends in PM2.5 air quality. However, the choice of method should be based on the specific objectives of the study and the nature of the data. Using the three methods may provide a more comprehensive assessment of air quality trends.
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