Exploratory Spatial Data Analysis (ESDA) untuk Identifikasi Pola Spasial PM10 (Partikel Debu Diameter kurang dari 10 mikron) dan Faktor yang mempengaruhi di Daerah Istimewa Yogyakarta

Kegiatan manusia seperti pembakaran, konstruksi, dan aktivitas transportasi menyumbang partikel-partikel polutan di udara, yang terakumulasi membentuk partikel debu dengan berbagai ukuran. Salah satunya berukuran 420Âµg/m3, pada suhu udara 35.5Â°C, kelembapan udara 41.0%, dan vegetasi kerapatan rendah, 2) Autokorelasi spasial positif menunjukkan pola spasial konsentrasi PM10 yang mengelompok, dengan kelompok nilai rendah (low-low cluster, cold spot) berada di Daerah Istimewa Yogyakarta bagian timur (Pegunungan Selatan) dan utara (Gunungapi Merapi), sedangkan kelompok nilai tinggi (high-high cluster, hot spot) berada di bagian barat dan tengah (dataran rendah antara Pegunungan Kulonprogo di sebelah barat dan Pegunungan Selatan di sebelah timur), 3) Pada beberapa tanggal pada kurun waktu Februari-Maret 2016, Mei-Juni 2017, November 2017, dan Mei-Juni 2018, dengan lokasi dan waktu yang berbeda, suhu udara, kelembapan udara, vegetasi, dan timbal berpengaruh positif dan negatif terhadap konsentrasi PM10 dengan pengaruh berturut-turut sebesar 0.5-50.0%, 0.9-35.0%, 1.1-38.0%, dan 85.5-95.3%.Human activities such as combustion, construction, and transportation activities contribute pollutant particles in the air, which accumulate to form dust particles of various sizes. One of them is less than 10Âµm sized called PM10. PM10 can endanger health because it is able to get in and settle in the respiratory system. Therefore, it is important analyze the PM10 phenomenon, especially in regions that are actually densely populated by activities such as the Special Region of Yogyakarta. Therefore, this study aims to 1) analyze the spatial distribution of PM10 concentrations along with air temperature, humidity, vegetation, and lead in several dates in 2016 to 2018, 2) analyze the spatial patterns of PM10 in the Special Region of Yogyakarta in several dates in the year 2016 to 2018, and 3) analyze the influence of air temperature, humidity, vegetation, and lead on PM10 in the Special Region of Yogyakarta in several dates in 2016 to 2018. This study uses ambient air quality data of the Special Region of Yogyakarta on several dates in February-March 2016, May-June 2017, November 2017, and May-June 2018 obtained through the Environment Agency of Special Region of Yogyakarta. PM10 data along with conditions of air temperature, humidity, and lead when measurements were processed and analyzed using the Inverse Distance Weighted (IDW) interpolation method, Moran's I spatial autocorrelation, Anselin's Local Moran's I cluster outlier analysis, and Getis-Ord Gi* hot spot analysis to produce PM10 maps that can describe the spatial patterns of PM10 in the Special Region of Yogyakarta, and Ordinary Least Square regression to analyze the effect of air temperature, humidity, vegetation, and lead on PM10 based on the coefficient values obtained. This whole method is part of the Exploratory Spatial Data Analysis (ESDA) analysis technique. The results showed 1) On several dates in the period of February-March 2016, May-June 2017, November 2017, and May-June 2018, the highest PM10 was on May 29, 2017 in the western part of Special Region of Yogyakarta (Wates Subdistrict, Kulonprogo Regency ) with the presence of concentrations >420Âµg/m3, at temperatures of 35.5Â°C, humidity of 41.0%, and low density vegetation, 2) Positive spatial autocorrelation shows clustering spatial patterns of PM10 concentrations, with clusters of low values (low-low cluster, cold spot) is in the Special Region of Yogyakarta in the east (South Mountains) and north (Merapi Volcano), while the clusters of high value (high-high cluster, hot spot) is in the west and center (the lowlands between the Kulonprogo Mountains in the west and the Mountains South to the east), 3) On several dates in the period of February-March 2016, May-June 2017, November 2017, and May-June 2018, with different locations and times, air temperature, air humidity, vegetation, and lead have positive and negative impacts on PM10 concentrations with successive impacts of 0.5-50.0%, 0.9-35.0%, 1.1-38.0%, and 85.5-95.3%.