Analisis Spasial dan Temporal Konsentrasi PM2.5 di Wilayah Urban dan Rural (Studi Kasus: Jakarta dan Puncak Bogor Tahun 2021-2024)

Abstract

Pencemaran udara oleh partikel halus menjadi permasalahan lingkungan signifikan di wilayah urban dan rural. Penelitian ini bertujuan menganalisis pola spasial dan temporal konsentrasi PM2.5 serta hubungannya dengan faktor meteorologi di wilayah urban (DKI Jakarta) dan rural (Puncak Bogor) periode 2021–2024. Data observasi dari lima stasiun di wilayah urban dan satu stasiun di wilayah rural dianalisis secara temporal, diurnal, dan musiman. Analisis spasial menggunakan data reanalisis GEOS-CF berdasarkan musim: DJF (musim hujan), MAM (musim peralihan I), JJA (musim kemarau), dan SON (musim peralihan II). Validasi dilakukan menggunakan korelasi Pearson, RMSE, dan MAE. Selain itu, konsentrasi PM2.5 juga dikorelasikan dengan faktor meteorologi. Hasil menunjukkan konsentrasi PM2.5 di wilayah urban (maksimum >100 µg/m³ saat JJA) lebih tinggi dibandingkan rural. GEOS-CF menunjukkan overestimate, dengan RMSE tertinggi di rural (65,99 µg/m³) dan korelasi tertinggi di urban DKI 1 saat SON (r = 0,497). Korelasi suhu terhadap PM2.5 di urban sebesar r = 0,391, dan di rural r = 0,468. Kecepatan angin berkorelasi negatif, r = –0,637 di urban dan r = –0,15 di rural. Polar plot menunjukkan konsentrasi tinggi di urban saat angin lemah dari arah sumber emisi, sedangkan di rural konsentrasi menurun saat angin kencang dan meningkat saat angin melemah di musim kemarau.

Air pollution from fine particles is a significant environmental issue in both urban and rural areas. This study analyzes the spatial and temporal patterns of PM2.5 concentrations and their relationship with meteorological factors in urban (Jakarta) and rural (Puncak Bogor) areas during 2021–2024. Observation data from five urban and one rural station were examined temporally, diurnally, and seasonally. Spatial analysis used GEOS-CF reanalysis data by season: DJF (rainy), MAM (transition I), JJA (dry), and SON (transition II). Validation applied Pearson correlation, RMSE, and MAE. PM2.5 was also correlated with meteorological factors. Results show higher PM2.5 in urban areas (max >100 µg/m³ in JJA) than rural. GEOS-CF overestimated, with the highest RMSE in rural (65.99 µg/m³) and highest correlation in urban DKI 1 during SON (r = 0.497). Temperature correlated with PM2.5 at r = 0.391 in urban and r = 0.468 in rural. Wind speed correlated negatively (r = –0.637 urban, r = –0.15 rural). Polar plots showed high urban concentrations during weak winds from emission sources, while in rural areas concentrations decreased during strong winds and increased during weak winds in the dry season.