MODEL SPASIAL SEBARAN PENCEMAR UDARA DARI SUMBER TRANSPORTASI DAN PENGARUHNYA PADA KUALITAS UDARA DI DALAM RUMAH DI SEKITAR JALAN RAYA (Studi kasus di Wilayah Karees dan Cibeunying Kidul, Bandung)
Abstract
Abstrak: Sektor transportasi, sebagai salah satu kontributor terbesar untuk gas rumah kaca, menjadi tantangan besar di abad 21 ini. Banyak juga pencemar udara lainnya yang diemisikan dari sektor ini, berdampak pada kesehatan manusia. Pengembangan pembangunan di sektor transportasi harus diarahkan menjadi pengembangan transportasi yang berkelanjutan dan memperhatikan aspek lingkungan dan kesehatan manusia. Oleh karena itu, di perlukan penelitian untuk mengembangkan metode dalam mengkuantifikasi dampak terhadap kesehatan manusia, di Indonesia dengan segala keterbatasan data yang ada. Tujuan dari makalah ini adalah mengembangkan pendekatan untuk memprediksi konsentrasi di dalam rumah yang berasal dari aktivitas transportasi terdekat. Dalam jurnal ini, analisa spasial akan dilakukan terhadap hasil model konsentrasi NO2 dan PM10, Model dispersi konsentrasi outdoor NO2 dan PM10 diperoleh dengan menggunakan CALINE4. Data input CALINE4 diantaranya data sekunder aktivitas transportasi dari 6 jalan, dihitung dengan faktor emisi UK-NAEI, data meteorologis, kordinat UTM reseptor "“ data penderita asma - dari penelitian sebelumnya. Persentase nilai kesalahan rata-rata dari model NO2, dari 120 reseptor di kawasan Cibeunying Kidul, adalah 33,3 % dan Nilai RMSE 18,5 µg/m3. Dari 3 reseptor di sekitar kawasan Karees, persentase nilai kesalahan rata-rata dari model PM10 adalah 29,89 % dan RMSE 1,02 µg/m3. Model konsentrasi NO2 dan PM10 adalah model 'factor of two'. Peta prediksi konsentrasi dalam rumah diperoleh menggunakan ArcGISd dengan metode interpolasi IDW. Persentase nilai kesalahan rata-rata dari model konsentrasi NO2 dalam rumah, dari 100 reseptor di kawasan Cibeunying Kidul, adalah 41,44 % dan Nilai RMSE 16,32 µg/m3. Dari 3 reseptor di sekitar kawasan Karees, persentase nilai kesalahan rata-rata dari model konsentrasi PM10 dalam rumah adalah 26,41 % dan RMSE 1,80 µg/m3. Model ini dapat digunakan untuk memprediksi konsentrasi NO2 dan PM10 dalam rumah di setiap reseptor untuk penelitian selanjutnya.)
Kata kunci: polusi udara, sektor transportasi, CALINE4, analisis spasial
Abstract : Transportation sector as one of the biggest contributors of greenhouse gas emission, becoming one of the biggest challenges in the 21st century. Many other air pollutants emit from this sector and have the human health effect. The development in this sector need to aims the sustainable transportation development, which concern the environment and human health aspects. Thus, need some research to develop the method to quantify the impact on human health, in Indonesia, where many data are limited and not available. This paper aims to develop an approach to estimate the pollutant concentration inside the house that is contributed by nearby transportation. In this paper, spatial analysis for the result modeling of NO2 and PM10. Outdoor NO2 and PM10 concentration dispersion model are obtained from CALINE4 software. Input data for CALINE4 are secondary data of transportation activiy from 6 road, calculated by UK-NAEI emission factor, meteorological data, the UTM coordinate of receptor -data of people with asthma - from previous study. The average percentage of error value from NO2 model, based on 120 receptor in Cibeunying Kidul area, is 33.3 % and RMSE is 18.5 µg/m3. From the 3 receptor around Karees area, the average percentage of error value from PM10 model is 28.89 % and RMSE is 1.02 µg/m3. NO2 and PM10 concentration model is a 'factor of two' model. The map of indoor concentration prediction are obtained using ArcGIS with IDW interpolation method,. The average percentage of error value from NO2 indoor concentration model, based on 100 receptor in Cibeunying Kidul area, is 41.44 % and RMSE is 16.32 µg/m3. From the 3 receptor around Karees area, the average percentage of error value from PM10 indoor concentration model is 26.41 % and RMSE is 1.80 µg/m3. The model can be used to predict the NO2 and PM10 indoor concentration in all receptors for further research..
Key words: . air pollution, transportation sector, CALINE4, spatial analysis
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