Kota besar seperti Jakarta memiliki masalah dalam mengelola kualitas udaranya. Dampak pencemaran udara akan mengakibatkan menurunnya kualitas kesehatan masyarakat. Dalam rangka pengendalian pencemaran udara diperlukan model jaringan stasiun pemantauan kualitas udara. Penelitian pemodelan jaring stasiun pemantau kualitas udara telah dilakukan untuk mencari lokasi yang potensial bagi stasiun pengamatan kualitas udara yang didasarkan pada model densitas populasi penduduk dan variasi spasial sumber pencemar di wilayah Jakarta. Pemodelan jejaring lokasi berpotensi untuk stasiun pemantauan kualitas udara dilakukan dengan dua pentahapan. Tahapan pertama adalah pemilihan lokasi potensi stasiun. Tahapan kedua adalah penyeleksian lokasi potensi stasiun berdasarkan zonasi, kepadatan penduduk, tutupan lahan sekitar, dan kemudahan akses dan perizinan. Pemodelan jaringan pemantauan kualitas udara menghasilkan luaran zona potensi titik pantau serta 81 titik potensi lokasi pemantauan kualitas udara. Potensi titik-titik tersebut diseleksi dengan mempertimbangkan landuse, jarak antartitik, dan kemudahan perizinan untuk mendapatkan 53 lokasi stasiun pemantauan udara untuk seluruh wilayah DKI Jakarta. Hasil pemodelan ini selanjutnya digunakan untuk menempatkan titik pemantauan kualitas udara pada riset Urban hybriD model for AiR pollution exposure Assessment (UDARA).

Background: Air pollution is an important risk factor for the disease burden; however there is limited evidence in Indonesia on the effect of air pollution on health, due to lack of exposure and health outcome data. The objective of this study is to evaluate the potential use of the IFLS data for response part of urban-scale air pollution exposure–health response studies. Methods: Relevant variables were extracted based on IFLS5 documentation review. Analysis of the spatial distribution of respondent, data completeness, prevalence of relevant health outcomes, and consistency or agreement evaluation between similar variables were performed. Power for ideal sample size was estimated. Results: There were 58,304 respondents across 23 provinces, with the highest density in Jakarta (750/district). Among chronic conditions, hypertension had the highest prevalence (15–25%) with data completeness of 79–83%. Consistency among self-reported health outcome variables was 90–99%, while that with objective measurements was 42–70%. The estimated statistical power for studying air pollution effect on hypertension (prevalence = 17%) in Jakarta was approximately 0.6 (α = 0.1). Conclusions: IFLS5 data has potential use for epidemiological study of air pollution and health outcomes such as hypertension, to be coupled with high quality urban-scale air pollution exposure estimates, particularly in Jakarta.

Emission inventory can be used as a tool to make policy decision including air pollution problem. Transportation sector has become the greatest pollutant source in urban area. The objective of this research is to compare emission load estimation using various emission factor databases with different characteristics. This research focused only on Nitrogen oxides (NOx) pollutant. The choice of pollutant is based on the reasons that it is the primary pollutant emitted from vehicle exhaust, the impacts on human health and the environment are well documented. First, transportation survey was conducted to get vehicles activity data such as volume with 7 vehicles classification and vehicles average speed. The survey was conducted at weekday and weekend condition. Then, Indonesia, England, and India factor emission database was chosen for determining emission load. Based on the transportation survey conducted and emission load calculation, it was known that the busiest road was Jalan Jakarta and was known to produce the highest emission load. It was known that emission factor value has a great influence to emission load. England emission factor database deemed to be probably the best emission factor because emission factor database which is more detail and is inacompliance with needs and site conditions, of course, will give a better emission load value.

Abstract Tropospheric ozone is harmful to human health and plants. It is resulted from photochemical processes involving NO x and VOCs from reactions of motor vehicle emissions and solar radiation in polluted urban environment. Historical data in Jakarta indicated that ozone concentrations often exceeded ambient standard threshold. To minimize its impact to human health it is important to predict its concentration. This paper reports the use of multivariate statistical method to predict ozone concentration, using precursor concentration and meteorological parameters. CH 4 , CO, NMHC, NO, NO 2 , THC data concentration, wind direction and speed, temperature, solar radiation and relative humidity during 2011 -2012 were used to build the model. Multiple linear regressions were applied to predict ozone concentration at Thamrin Station, Jakarta. These data were used as predictors at time (t) to estimate the ozone concentration at time (t +1). Meteorological conditions were found to strongly affect the concentration of ozone. The strongest relationship was found between ozone and temperature (0.513, p = 0.000). Weaker but significant positive correlations were found for solar radiation and NO 2 (r = 0.242, p= 0.000),. NMHC and NO correlation (r= 0.353, p= 0.000). Both NO and NMHC are freshly emitted from exhaust gas. Correlations between humidity, wind speed and direction were negative. Methana, NMHC, were negatively correlated with ozone due to their roles for producing NO 2 as the main precursor, while NO was for its scavenging reaction with O 3 . Based on Adjusted R 2 value, all predictors could explain variation in ozone concentration of approximately 46.32%. These findings will be useful as input in urban transportation planning and management in cities with tropical climate like Indonesia, as all precursors are emitted from vehicle combustion.

The basin shape of Bandung limits dispersion and transport of air pollutants. This topographical characteristic causes air pollutants to be trapped and accumulated within the basin, where urban areas were located. In the issue of of sustainable city, rainwater could be potential sources of fresh water. However, air pollution in urban area might alter the natural rainwater composition. Characterization of rainwater was conducted by collecting rainwater bulk samples at 4 (four) sites located in a transect from high elevation to the lowest at the base of the Bandung basin. Identification of trace metals, cations and anions were performed in rainwater collected in Bandung City during rainy seasons in February to March 2016. Acidity (pH), conductivity, anions and cations (SO 4 2-, NO 3