East Nusa Tenggara is one of the provinces in Indonesia that has big forest fires following some provinces in Kalimantan and Sumatra. However, forest fires in East Nusa Tenggara have less attention in forest fires discussion in Indonesia. This study aims to analyze forest fires in East Nusa Tenggara and their impact on reducing visibility and increasing carbon monoxide (CO) from 2015 to 2019. In this study, hotspot, forest fire area, Oceanic Niño Index, visibility, and CO total column data were used to analyze the forest fires using a statistical comparison method in East Nusa Tenggara, Kalimantan, and Sumatra. The result shows that the number of hotspots in East Nusa Tenggara less than in Kalimantan and Sumatra for the same forest fire area. The forest fires in East Nusa Tenggara do not harm the atmospheric environment significantly. East Nusa Tenggara dominantly consists of savanna areas with no peatland, hence, the forest biomass burning produces less smoke and CO. Furthermore, the forest fire in East Nusa Tenggara has not an impact on decreasing visibility and increasing CO total column, in contrast, visibility in Sumatra and Kalimantan has fallen to 6 km from the annual average, and CO total column rise three times of normal condition during peak fire.
A comparison of the measurement results between the active method and the passive method was carried out to see the correlation between the resulting concentrations. The passive method used CSIRO passive sampler, while the active method used the Air Quality Monitoring System (AQMS). Sampling was conducted at AQMS Bundaran HI Jakarta Station, belonging to the Environment Laboratory of DLH DKI Jakarta. The sampling period was February - April 2019 for SO2 and NO2 parameters, with a sampling duration of three days for each data. Data was proceed using the correlation method. Data filtering with boxplot was used to filter outlier data from the passive sampler and AQMS measurements. Meteorological factors were included in the correlation calculations because of their effect on gas absorption that occurred in the passive sampler. Meteorological factors used were temperature, humidity, and wind direction. The AQMS concentration value prediction was calculated using the correlation equation between the passive sampler and the AQMS. The results showed that the correlation coefficient value between the passive sampler and AQMS was 0.67 for SO2 and NO2 of 0.79. Multivariate correlation using meteorological data, to improve the correlation value, obtained correlation values of 0.97 for SO2 and 0.94 for NO2. The predictive value of AQMS used a regression equation, with an average bias value of 4.4% for SO2 and 9.9% for NO2, while the RMSE values were 0.89 for SO2 and 4.41 for NO2. The results showed that the concentration of SO2 and NO2 gas measurement results from the passive and active methods had a good and significant correlation. Keywords: passive method, active method, SO2, NO2, AQMS ABSTRAK Perbandingan hasil pengukuran antara metode aktif dan metode pasif dilakukan untuk melihat korelasi konsentrasi yang dihasilkan antara metode aktif dan metode pasif. Metode pasif menggunakan passive sampler CSIRO, sedangkan metode aktif menggunakan Air Quality Monitoring System (AQMS). Sampling dilakukan di Stasiun Pemantauan Kualitas Udara (SPKU) DKI 1 Bundaran HI milik Laboratorium Lingkungan Hidup DLH DKI Jakarta. Periode sampling dilakukan dari bulan Februari – April 2019 untuk parameter SO2 dan NO2, dengan durasi sampling per tiga hari untuk satu data. Pengolahan data dilakukan dengan metode korelasi. Filter data dilakukan dengan menggunakan boxplot untuk memfilter data outlier dari pengukuran passive sampler dan AQMS. Faktor meteorologi dimasukkan dalam perhitungan korelasi karena pengaruhnya pada penyerapan gas yang terjadi di passive sampler. Faktor meteorologi yang digunakan adalah temperatur, kelembapan, dan arah angin. Prediksi nilai konsentrasi AQMS dihitung dengan menggunakan persamaan korelasi antara passive sampler dengan AQMS. Hasil yang diperoleh menunjukkan nilai koefisien korelasi antara passive sampler dengan AQMS sebesar 0,67 untuk SO2 dan NO2 sebesar 0,79. Korelasi multivariat menggunakan data meteorologi untuk memperbaiki nilai korelasi diperoleh nilai korelasi 0,97 untuk SO2 dan 0,94 untuk NO2. Nilai prediksi AQMS menggunakan persamaan regresi, dengan nilai rata-rata bias 4,4% untuk SO2 dan 9,9% untuk NO2, sedangkan nilai RMSE sebesar 0,89 untuk SO2 dan 4,41 untuk NO2. Hasil penelitian menunjukkan bahwa konsentrasi hasil pengukuran gas SO2 dan NO2 metode pasif dan metode aktif mempunyai korelasi yang baik dan signifikan. Kata kunci: metode pasif, metode aktif, SO2, NO2, AQMS
It has been analyzed impact of forest fire on the air quality using PM10 parameter and visibility during 2000 -2014 in Palangka Raya, Central Kalimantan province. Palangka Raya is an affected forest fire area with a monsoonal rainfall type which has one peak of the rainy season in January and one peak of the dry season in August. Drought condition has an impact on rising forest fire intensity causes increasing of PM10 concentration and decresing of visibility in July to November moreover when there is an El Niño phenomenon. The result of PM10 analysis shows that the air quality index in Palangka Raya during December -June is in a good level category and still below the ambient air quality standard with an average concentration of 19 µg/m3. The impact of forest fire on declining air quality due to increasing of PM10 concentration occurred in July -November with an average concentration rising of 129 µg/m3. The El Niño phenomenon rises the PM10 concentration due to increasing of forest fires, but the increasing of PM10 is not comparable to the strength of El Niño, because of combustion condition and and human activities that play a role in forest fires. The worst impact of El Niño occurred in 2002, although the El Niño strength was only moderate, which is a half the time from July to November Palangka Raya covered air quality with dangerous levels with PM10 concentrations of more than µg/m3. A high PM10 concentration environment reduces the visibility significantly, which is visibility in the no fire condition about 8 km, but when the huge forest fire the visibility drops to 0.1 km.
Forest fires have an impact on air quality and visibility. Visibility can be associated with a highly visual indicator of air pollution. This research aims to analyze the relationship between the PM10 concentration and visibility during the forest firest events and normal conditions in Palangkaraya from 2000 to 2014 by using a regression method. The relative humidity data was used to filter the PM10 and visibility. Furthermore, the equation resulted from the regression analysis was used to predict PM10 concentration in Palangka Raya. The result showed that the regression pattern tends to form a logarithmic function. Specifically, without filtering data, the coefficient correlation (r-value) during the forest fire events and normal conditions are 0.69 and 0.5, respectively. Meanwhile, a data filtering method gives a higher relationship between PM10 and visibility, with the r-value of 0.7 for the forest fire events and 0.68 for the normal condition. On the other hand, the prediction of PM10 concentration indicates a high bias value due to the other influenced factors that have not been included in this study.
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