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Komposisi Kimia Pencemar Partikulat Kasar dan Halus di DKI Jakarta Pada Musim Hujan dan Musim Kemarau Driejana Driejana; Andi Iin Nindy Karlinda Kadir; Muhayatun Santoso
Jurnal Ilmu Lingkungan Vol 18, No 3 (2020): November 2020
Publisher : School of Postgraduate Studies, Diponegoro Univer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/jil.18.3.522-530

Abstract

Partikulat yang memberikan dampak negatif terhadap lingkungan dan kesehatan manusia dikategorikan berdasarkan ukurannya yaitu PM10 berukuran <10 μm dan PM2.5 berukuran <2,5 μm.  Dampak terhadap kesehatan akan semakin besar pada ukuran partikulat yang semakin kecil, serta tergantung pada komposisi kimia yang dikandungnya. Penelitian ini difokuskan untuk mengetahui perbedaan komposisi kimia partikulat halus (PM2.5) dan partikulat kasar (PM10-2,5) pada musim hujan dan musim kemarau, serta sumber-sumber pengemisinya. Sampling dilakukan di DKI Jakarta menggunakan alat Gent stacked filter sampler unit pada musim hujan. Hasil pengukuran total massa partikulat dan komposisinya dibandingkan dengan pengukuran pada studi sebelumnya yang dilakukan pada musim kemarau. Massa partikulat ditentukan menggunakan alat neraca semi Mikro Mettler Toledo. Untuk mengetahui unsur-unsur yang terkandung di dalam filter kasar maupun halus digunakan Epsilon 5 EDXRF spectrometer. Analisis korelasi pada komposisi kimia digunakan untuk memprediksi sumber-sumber pengemisi. Hasil perhitungan konsentrasi rata-rata PM2,5 dan PM10-2,5 lebih rendah pada musim hujan dibandingkan dengan pada musim kemarau. Konsentrasi rata-rata partikulat halus di musim hujan adalah sebesar 15,31±0,41 µg/m3 dan partikulat kasar sebesar 28,69±0,56 µg/m3 sedangkan di musim kemarau sebesar 26,76±0,22 µg/m3 dan 35,05±0,28 µg/m3.  Hasil uji t menunjukan bahwa pada musim hujan dan musim kemarau terdapat perbedaan yang signifikan pada komponen kimia penyusun partikulat halus, yaitu BC, Al, Si, S, K, Ca, Ti, Ni, Zn, As.  Untuk partikulat kasar unsur yang menunjukkan perbedaan signifikan adalah Al, Si, S, K, Ca, V, Ni, Cu, As, Cl. Perbedaan konsentrasi rata-rata ini kemungkinan disebabkan oleh terjadinya deposisi basah. Berdasarkan analisis sumber pencemar,  PM2,5 ¬diprediksi berasal dari debu tanah/soil, emisi kendaraan dan pembakaran biomassa serta industri, sedangkan PM(10-PM2,5) bersumber dari garam-garam lautan (sea salt), debu tanah, dan industri.ABSTRACTParticulate matters (PM) have negative impacts on the environment and human health. PM were categorized based on their size, namely PM10 with size <10 μm (coarse) and PM2,5 with size <2.5 μm (fine). The impact on health will be greater at the smaller particulate size, and depending on their chemical composition. This study is focused on the chemical composition of fine and coarse particulate matter in the rainy and dry seasons as well as their potential sources. Sampling was carried out in DKI Jakarta using a Gent stacked filter sampler unit during the rainy season. The measurement results of total particulate mass and its composition were compared with measurements of a previous study conducted during the dry season. The particulate mass was determined using a Mettler Toledo semi-balance instrument. Furthermore, to determine the elements contained in the coarse and fine filters, an Epsilon 5 EDXRF spectrometer was used. Correlation analysis of the chemical composition were used to predict the emission sources. The results demonstrated that the average concentration of PM2,5 and PM(10-2,5) were lower in the rainy season than in the dry season. The average concentration of fine particulates in the rainy season was 15,31 ± 0,41 µg/m3 and coarse particulates was 28,69 ± 0,56 µg/m3. In the dry season it was 26,76 ± 0,22 µg/m3 and 35,05 ± 0,28 µg/m3. The t-test result showed that there was a significant difference between fine particles composition in the rainy season, particularly for BC, Al, Si, S, K, Ca, Ti, Ni, Zn, As. For coarse particulates, the elements that show significant differences were Al, Si, S, K, Ca, V, Ni, Cu, As, Cl. The concentration difference was likely due to wet deposition. Based on the analysis of pollutant sources, PM2,5 was predicted to come from soil dust, vehicle emissions and combustion of biomass and fuel industry, while PM (10-PM2,5) (coarse particles) came from sea salt, ground dust, and industry.
The Role of Vehicle Speed Management in Urban Air Pollutant Emission Reduction Adyati Yudison; Driejana Driejana; Iman K. Reksowardojo; Aminudin Sulaeman
IPTEK Journal of Proceedings Series No 6 (2017): The 3rd International Conference on Civil Engineering Research (ICCER) 2017
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (141.198 KB) | DOI: 10.12962/j23546026.y2017i6.3299

Abstract

Lack of mass transit facilities in urban area leads motorcycle to become the most chosen vehicle mode in Indonesia. It accounts for approximately 80% of the total motorized vehicle population. Increasing in motor vehicle population causes traffic congestion and lower traffic speed, hence higher emissions that worsen urban air quality.  This paper aims to investigate relationship of vehicle travelling speeds and CO2, CO and HC emission profiles of the typical motorcycle used in Indonesia. Exhaust emissions of 20 motorcycles were tested using autocheck analyzer on idle and speed variations of, 10, 20, 30, 40, 50, and 60 kph on a chassis dynamometer. For idle condition average emission rates of CO2, CO and HC were 0.16, 0.03 and 0.002 g/s, respectively. The results show that the highest CO2 and HC emissions of 82.30 g/km and 0.55 g/km, respectively, found at 10 kph. The lowest emissions were found at 60 kph speed with CO2 emission rate of 27.03 g/km and HC of 0.10 g/km. Carbon monoxide (CO) showed slightly different emission pattern, with 12.85 g/km found at 20 kph  and 4.18 g/km at 50 kph. In general, the result shows that the higher the speed the lower the CO2, CO, and HC emissions. Emission profiles indicate that the speed between 30-50 km/h could be recommended as the most suitable traffic speed in urban area to ensure mobility, safety as well as the lowest emissions. The results are valuable as input to transportation management as a part of environmentally sustainable transportation.
Kinetika Formaldehida (HCHO) Dan Ozon (O3) Di Daerah Urban (Studi Kasus Jakarta): (CASE STUDY: JAKARTA) Nadiyatur Rahmatikal Wasiah; Driejana Driejana
JURNAL RISET KESEHATAN POLTEKKES DEPKES BANDUNG, Online ISSN 2579-8103 Vol 12 No 1 (2020): Jurnal Riset Kesehatan Poltekkes DepKes Bandung
Publisher : Poltekkes Kemenkes Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (335.495 KB)

Abstract

Formaldehyde (HCHO) is a toxic compound and plays an important role in atmospheric chemical reactions as a source of radicals and precursor of oxidants (mainly ozone). HCHO generates from primary sources (motor vehicles) and secondary sources (photochemical reactions). However, carbonyl compounds monitoring and research on their roles in chemical reaction (ozone production) in Indonesia is still limited. This research investigated the contribution and relationship of hydrocarbons (formaldehyde) and ozone in urban areas. Formaldehyde measurements were carried out for two weeks using absorption method and samples were analyzed by spectrophotometric. Two empirical methods were used to predict ozone production, namely MIR (maximum incremental reactivity) method and propane equivalent method. MIR is a method to calculate organic compounds reactivity in ozone formation. Meanwhile, propane-equivalent method aims to determine ozone estimate using the rate of hydrocarbons oxidation (formaldehyde and propane). Based on ozone diurnal variation, the MIR method provided overestimation, while the propane equivalent method show underestimate predictions. The mean value ​​of ozone concentrations as the reference data in µg/m3) was 34.39 , while estimates resulted in 83.93 (MIR method) and 9.92 (propane equivalent method), respectively. RMSE (Root Mean Squared Error) calculated the error range of the two methods found the values of 81.23 µg/m3 (MIR) and 31.90 µg/ m3 (propane equivalent). It is found that these methods did not predict ozone well. However, both method were easy to applied and could estimated ozone concentration although the information of hydrocarbons data were limited. it is suggested that alternative method were applied by adding meteorological data and other hydrocarbons concentrations to produce better prediction ozone model