The chemical composition of acid deposition shows that ammonium and chloride concentrations as the indicators of forest fires were higher than sulfate and nitrate in Sumatera areas such as Medan, Lampung, Palembang, and Kototabang. Chloride had higher concentration than sodium (Na+ sea originated) with the ratio value of Cl−/Na+ > 1.16 found in Medan and Palembang. Ionic compositions from the lowest to the highest concentration in Kototabang were H+ > Cl− > Na+ > NH4+ > nss-Ca2+ > K+ > NO3- > nss-SO42- > Mg2+ > ss-SO42- > ss-Ca2+. Acid rain takes place if the acid compounds such as sulfates, nitrates, and chlorides dominate. If the ratio value of NO3-/(nss-SO42- + NO3-) < 0.5 then it indicates that nss-SO42- is higher than NO3-. Between 2001 and 2010 it was found that the frequency value of NO3-/(nss-SO42- + NO3-) < 0.5 was 97% from annual mean of 34 pieces of data in Medan, Kototabang, Lampung, and Palembang. Forest fires influence was more dominant than anthropogenic activities in Kototabang, Palembang and Lampung, except in Medan. It showed that ammonium was higher than NO3- content if the ratio value of NO3-/(NH4+ + NO3-) < 0.5 was 62%. For the period 2001–2010 the frequency value of NO3-/(NH4+ + NO3-) < 0.5 was 74% from total 34 annual mean pieces of data in four locations, that is, Medan, Kototabang, Palembang, and Lampung.

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

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<p>Measurement of ozone concentration in ambient air was carried out using the passive sampling method in Cipedes, Bandung, from 2012 – 2020. Sample analysis was done using ICS 1500 Dionex ion chromatography. The results showed a fluctuating concentration from 2012 -2020 with the highest average value in 2015 of 29.94 g/m³. The monthly pattern shows the highest ozone concentration in September and the lowest in December; this condition was related to the intensity of rainfall that can clean ozone in the atmosphere. The seasonal pattern showed in the dry season ozone concentration is relatively higher than in the rainy season. A comparison of passive and continuous sampling was made to see the performance of the passive sampler showing a similar pattern with a correlation coefficient of r = 0.48. This difference in value was related to the absorption of ozone gas in the passive sampler absorbing filter and the meteorological factors.</p>