Meteorological baseline for air-quality assessment in a very wet tropical lowland
DOI:
https://doi.org/10.61511/evojes.v3i2.2026.3919Keywords:
air quality, Central Kalimantan, Kapuas Regency, meteorological baseline, NASA POWER, Schmidt-Ferguson climate classification, tropical lowlandAbstract
Background: Meteorological baselines are essential for interpreting ambient air-quality dynamics in humid tropical lowlands because rainfall, humidity, temperature, and wind jointly regulate wet deposition, pollutant dilution, atmospheric stagnation, and smoke transport. This study characterizes the climatic and dispersion-relevant meteorological setting of the PT United Agro Indonesia study area in Kapuas Regency, Central Kalimantan, Indonesia. Methods: Monthly meteorological data for 2011-2024 were compiled from the NASA POWER source-native dataset at -2.5486° latitude and 114.6967° longitude, approximately 5 m above sea level. Rainfall was classified using the Schmidt-Ferguson Q index, and rainfall, 2-m air temperature, relative humidity, wind direction, and wind speed were evaluated through descriptive climatological analysis. Findings: The study area had an average of 0.86 dry months, 0.64 humid months, and 10.50 wet months per year, producing a Q value of 8.16 and a Type A very wet climate. Mean annual rainfall was 2,878 mm, with the highest annual total in 2020 (3,865 mm) and the lowest in 2023 (2,245 mm). Mean annual air temperature was 26.5 °C, relative humidity averaged 88.4%, and monthly wind speeds were consistently very low, with a mean of 0.088 m s-1. Southeast and east winds dominated the monthly wind field. Conclusion: The site is persistently humid but still has a mid-year to early late-year rainfall minimum and weak atmospheric ventilation, conditions that can intensify pollutant accumulation when biomass burning, road dust, or operational emissions occur. Novelty/Originality of this article: The article provides a site-specific, meteorological baseline for air-quality assessment in a data-sparse tropical lowland by integrating Schmidt-Ferguson climate classification with dispersion-relevant humidity and wind metrics.
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