Uji Model Aeromod Terhadap Sebaran Particulate Matter 10 µm (PM10) di Sekitar Kawasan PT Semen Padang

Penelitian ini bertujuan untuk menguji model AERMOD dalam memprediksi sebaran PM10 di udara ambien kawasan PT Semen Padang. Lokasi penelitian sebanyak 32 titik berdasarkan 8 arah mata angin dengan jarak 0,5 km, 1 km, 1,5 km dan 2 km dari PT Semen Padang. Pengukuran PM10 menggunakan EPAM 5000 Real Time Particulate Air Monitor dilanjutkan pemetaan dengan software Surfer 11. Waktu pengukuran dibagi menjadi 4 shift, yaitu shift 1 (00.00 – 05.59 WIB), shift 2 (06.00 – 11.59 WIB), shift 3 (12.00 – 17.59 WIB) dan shift 4 (18.00 – 23.59 WIB). Pengambilan data meteorologi (temperatur udara, tekanan udara, kelembapan, kecepatan angin dan arah angin) menggunakan alat Meteorological Station PCE-FWS-20 untuk input data pada AERMET, dilanjutkan prediksi sebaran PM10 menggunakan software AERMOD View 8.9.0. Hasil penelitian menunjukkan konsentrasi PM10 dengan EPAM 5000 berkisar antara 21,0 – 79,0 µg/m3 dengan rata-rata 24 jam sebesar 41,7 µg/m3. Konsentrasi PM10 dengan AERMOD berkisar antara 3,5 sampai 68,0 µg/m3 dengan rata-rata 24 jam sebesar 10,6 µg/m3. Jika dibandingkan dengan baku mutu untuk Peraturan Pemerintah No. 22 Tahun 2021 tentang Penyelenggaraan Perlindungan dan Pengelolaan Lingkungan Hidup, lokasi 11 dengan koordinat S 0°56'52.46" dan E 100°27'41.88" pada jarak 1 km kawasan Barat PT Semen Padang tidak memenuhi baku mutu. Model mendekati ideal atau dikatakan sempurna yaitu lokasi arah Timur dan Timur Laut karena elevasi yang lebih tinggi dari sumber emisi dan merupakan arah angin dominan pada siang hari.ABSTRACTThis study aims to test the AERMOD model in predicting the distribution of PM10 in the ambient air of the PT Semen Padang area. The research locations were 32 points based on eight cardinal directions with a radius of 0.5 km, 1 km, 1.5 km, and 2 km from PT Semen Padang. PM10 measurement using EPAM 5000 Real-Time Particulate Air Monitor followed by mapping with Surfer 11 software. The measurement time is divided into four shifts, namely shift 1 (00.00 – 05.59 WIB), shift 2 (06.00 – 11.59 WIB), shift 3 (12.00 – 17.59 WIB), and shift 4 (18.00 – 23.59 WIB). Meteorological data retrieval (air temperature, air pressure, humidity, wind speed and wind direction) using the Meteorological Station PCE-FWS-20 for data input to AERMET, followed by prediction of PM10 distribution using AERMOD View 8.9.0 software. The results showed that the concentration of PM10 with EPAM 5000 ranged from 21.0 – 79.0 g/m3 with a 24-hour average of 41.7 g/m3. The concentration of PM10 with AERMOD ranged from 3.5 - 68.0 g/m3 with a 24-hour average of 10.6 g/m3. When compared with the quality standard for Government Regulation no. 22 of 2021 concerning the Implementation of Environmental Protection and Management, location 11 with coordinates S 0°56'52.46" and E 100°27'41.88" at a distance of 1 km west of PT Semen Padang does not meet the quality standards. The model is close to ideal or is said to be perfect, namely the location of the East and Northeast directions because of the higher elevation of the emission source and the dominant wind direction during the day.

Analyzing the health risk assessment of particles in Isfahan Steel Company by AERMOD model

Introduction: One of the effects of air pollution in the community was increasing mortality rate. Determination of contamination was the first step in improving the existing conditions. Therefore, the way of pollutants distribution and the timing and spatial changes were important. This study aimed to evaluate the risk of Parental Emissions (PE) of Isfahan Steel company using AERMOD. Materials and methods: In this research, the distribution of suspended particles of the Isfahan Steel company were modeled in the AERMOD for 1 h, 24 h and yearly average (30×30 km2), then the comparison of the average concentrations modeled with air standards clean country and Environmental Protection Agency (EPA) regional risk maps were provided in Arc GIS. Results: The prediction of the distribution of 24-h mean concentrations indicated that the maximum value for the 24-h average was equal to 8.52 EPA and 25.25 times, the standard Iran's clean air. Also, the prediction of the distribution of average annual concentrations indicated that the maximum value for the average annual time was 91.1 times, the EPA standard and 4.78% higher than Iran's clean air standard. Conclusion: Health risk maps show that the risk spot was not regional in the direction of the region's wind and topography of the region was the main factor in the distribution of risky spots in the region. Legitimate use of the AERMOD could be useful in managing, controlling, and evaluating air pollutants especially in industrial units of the country.

Effect of Cement Industry on Ambient Air Quality and Potential Health Risk: A Case Study from Riyadh, Saudi Arabia

This study aimed to determine the cement industry's impact on ambient air quality inside and around a Saudi Arabian cement plant. Air quality has assessed in terms of several indicators: carbon dioxide, carbon monoxide, nitrogen dioxide, sulfur dioxide, PM10, PM2.5, ozone, and volatile organic compounds. AERMOD model was used to predict the concentrations of pollutants in the surrounding area. Results obtained revealed that the concentration of all impurities is within the standard limits for ambient air quality. In comparison with OSHA guidelines, only PM10 concentration exceeded the allowable limit. The higher concentrations of pollutants are recorded at the site closest to the plant site (S1, a housing compound located 0.8km ESE from the plant). Concentrations at the other monitoring sites decreased significantly. Except for PM10, the calculated hazard quotient (HQ) of all pollutants was <1which indicated no health effects are expected. The HQ of emissions can be ranked as: PM10> PM2.5>CO2>O3>CO>VOCs>NOx>SO2>H2S. The hazardous index (HI) was: 3.59, 2.76, 2.18, and 2.67 for S1, S2 (located 17km NNE), S3 (located 10.6km SE), and S4 (located 6.4km SSW), respectively. The affected organs can be ranked based on health risk calculation as respiratory system>cardiovascular system>Eye irritation>Allergy infection>Nervous system>Development>Hematology >Alimentary endocrine. The cancer risk factor was shallow and ranged from 4.04x10-6 for S4 to 1.88x10-5 for S1, which indicated a very low-risk potential. In terms of emissions concentrations, AERMOD predicted higher concentrations than the actual monitoring data for all measured parameters.

Dispersion Model Evaluation of SO2 Emission From Stack in Oil Refinery Plant Using AERMOD 8.9.0

Background: In the operation of oil refineries, one of the main pollutants in stack emission is SO2. Dispersion modeling is a necessary tool for simulation of air pollutant concentration, which is the main part of urban air quality management. Objectives: The ability of air quality models is well established where sufficient input data are available. The present study is performed to assess the SO2 emission from the Tehran oil refinery. Methods: The release pattern of SO2 was simulated by the AERMOD model in the desired zone, with an area of 25 × 25 km2. Modeling was run in the 1, 3, 8, and 24 average times for two warm and cold seasons. Predicted and observed pollutant concentrations were compared for validation of the results by the EPA statistical index. Four receptors were selected to compare the predicted and observed values. Results: Correlation coefficient values for SO2 were 0.92 and 0.95 for the warm and cold seasons, respectively. The maximum concentration of SO2 was on the local scale of 25 × 25 km2. Conclusions: The results showed that modeling is appropriate for conducting point sources in the oil refinery. 1 and 24 h averaging time from the model for SO2 concentrations were lower than standard levels; therefore, in the study area, the AERMOD model performance for prediction of SO2 concentrations was acceptable. Although most of the measurements were lower than standard values, due to the possibility of air pollution transmission to the urban area, their control should be considered.

An analysis of air pollution at some industrial areas of Kano using the AERMOD Model

The effect of pollution on air quality has been a concern for mankind for a long time. In some cases the problem is essentially one of local emissions in a given urban area leading to an adverse effect on air quality in that same area. However, in the general case, the problem is more diverse in that the problem of air pollution has multiplicity effects beyond the point source and these effects are dynamic in nature. Such effects are usually evaluated using dynamical equations. In this study, a comprehensive review on effect of air polluting variables was described on the basis of evaluation of formulation equations of the American Meteorological Society and U.S. Environmental protection Agency Regulatory Model (AERMOD view 9.6.5). The AERMOD model was also used to simulate the dispersion and deposition of the hourly and daily H2S and NO2 concentrations from two domains: Challawa and Sharada industrial estates /areas respectively. The AERMOD model evaluation showed that there was good correlation between the modelled and observed H2S concentration for the daily and hourly comparison at Challawa (0.53 and 0.91 respectively) but the daily and hourly comparison of H2S at Sharada (0.13 and 0.46 respectively) was seen to drop indicating poor correlation and model skill. However, model evaluation of NO2 shows poor agreements and model skill at Challawa as well as daily comparison at Sharada. However, the modelling shows good agreement (R2= 0.64) in the trend for the hourly value modelled versus observed concentrations at Sharada. Moreover, the mean absolute percentage error (MAPE) for the two pollutants (H2S and NO2) at all the two domains indicates highly accurate result for both daily and hourly concentrations. AERMOD software can therefore be used to estimate the dispersion and deposition of the pollutants at some domains considered in this study. Key Words: AERMOD model, Air pollutant, Industrial sources, Dispersion and Deposition

Emission Characteristics and Risk Assessment in an Industrial Metropolitan Area in Taiwan using the AERMOD Model

Air toxics, also known as hazardous air pollutants (HAPs), have gained increased public awareness in recent years. Air toxics may be released from various sources, such as mobile sources, stationary sources, and fugitive emissions. This study investigated profiles of air toxics from mobile sources, stationary sources, and the operations in a port in an industrial metropolitan area in Taiwan. Six carcinogens, including benzene, formaldehyde, 1,3-butadiene, arsenic, 2,3,7,8-TCDD, and diesel particulate matter (DPM), were chosen as the target pollutants. The AERMOD model was applied to simulate the concentrations of the ambient air toxic species, and the concentrations were used to evaluate cancer risk. Cancer risk for each air toxic was also investigated to evaluate the potential impact on residents. The results of the emission estimation for the base year (2014) showed that the emissions of benzene, 1,3-butadiene, and formaldehyde could be mainly attributed to mobile sources in the study areas. The contributions, in order, were 86 %, 77 %, and 69 %. DPM emissions from port operations accounted for 76 %, and most of the arsenic (70 %) and 2,3,7,8-TCDD (99 %) were emitted from stationary sources, especially from the steel industry. Approximately 66 % and 32 % of the cancer risk of air toxics were contributed to the emissions from port operations and on-road vehicles, respectively, in this area, and approximately 1.4 % of the risk was contributed to stationary sources. DPM was the pollutant that posed the highest cancer risk among all six air toxics. It accounted for more than 80 % of the overall cancer risk, followed by 1,3-butadiene (10 %), benzene (4.7 %), formaldehyde (1.2 %), arsenic (0.7 %), and 2,3,7,8-TCDD (0.2 %). The dominant sources of DPM were ocean-going vessels and diesel trucks.