عنوان مقاله [English]
One of the major air pollutants, in particular particulate matter (PM) with an aerodynamic diameter of less than 2.5 microns (PM2.5), remains as a pervasive problem in spite of outstanding efforts to reduce and control it.
Satellite-based measurement is used in many studies. A commonly used satellite product to estimate the ground concentration of PM2.5 is aerosol optical depth (AOD). AOD is an optical measurement method that describes the frequency of aerosol and their extinction factors in an integrated column in the atmosphere, which is commonly obtained from satellites and ground instrumentation. Regression statistical methods such as simple linear regressions to complex multivariate regressions (including boundary height elevation, temperature, relative humidity and wind) have been used to establish a relationship between AOD and PM2.5 in literature.
In particular, the Advanced Sensor MODIS is used to obtain aerosol optical depth on a global scale for long-term. Many studies have been using MODIS AOD products to achieve particulate matter concentrations.
The purpose of this study is to estimate the annual mass concentrations of PM2.5 particles using regression and satellite data, investigate the seasonal variation trend of the aerosol optical depth on Tehran and the concentration of PM2.5 particles measured by the air quality monitoring station of the Air Quality Control Company during one year.
Materials & Methods
The amount of PM2.5 concentration in Tehran in recent years, especially in the late fall and early winter, is in many cases above the standard limit, which sometimes provoked a warning and even a dangerous situation.
In this study, the MOD04_L2 product (Collection 6.1 level 2) of MODIS on the Terra satellite at 550 nm (https://worldview.earthdata.nasa.gov) with spatial resolution 3 × 3 km was used. AOD data were retrieved from 8:30 to 10:30 (corresponding to 12-14 local time) for a one-year period (from 22/6/2017 to 21/6/2018). By extracting the AOD values for Tehran (and also extracting the same latitude and longitude optical depths as well as each ground air pollution measurement station), the amount of daily optical depth of each station was determined. In this study, PM2.5 mass concentrations (in micrograms per cubic meter) were used which measured by the BAM device at the Air Quality Control Company (AQCC) stations for the one-year period. It should be noted that the 12 to 14 measured concentrations were used to correspond with the satellite's time from the study area. The rest of the monitoring stations of the air quality control company had not any data during the study period.
Also, by using an Inverse Distance Weight (IDW) method at a 12 km radius around each air pollution monitoring station, the concentration of PM2.5 was predicted in other parts of Tehran without a station.
AOD and PM2.5 daily data (during the one-year period) were used to determine regression relations for each station. In this method, which was applied independently to each of the stations, the data for one month were discarded from each station, and the regression equation was established between the remaining 11 months data. Then, the PM concentration values for the separated moon were predicted by the obtained regression relationship to determine the ability of the relationship to estimate the concentrations that were not used in the regression equation. Finally, the annual concentrations of each station were obtained by averaging the estimated daily values. This operation was repeated for all stations individually to predict the average annual concentration of particulate matters in all stations. It should be noted that for determining the regression equations for each month (at each station), the AOD and PM2.5 data were used when the two parameters had a value.
Results and Discussion
The annual AOD value of the total stations with average of 0.23 and standard deviation of 0.01 varied from 0.20 to 0.25, which was the highest average (0.24) for the autumn between all seasons. Also, the maximum and minimum values of AOD (0.29 and 0.06, respectively) were related to spring. The PM2.5 annual concentration of all stations with a mean of 27.09 μg/m3 and a standard deviation (SD) of 5.16 ranging from 20.10 to 36.14 μg/m3, which was the highest average among the seasons (34.95 μg/m3) in fall. The highest (71 μg/m3) and the lowest (4.33 μg/m3) PM2.5 measured concentration were for autumn and spring, respectively. The maximum average amount of AOD and PM2.5 data measured (among all stations) was related to fall.
As stated above, the average annual PM2.5 concentration was predicted for areas in Tehran without a station using an IDW interpolation method at a 12 km radius around each air pollution monitoring station. Accordingly, the areas around the stations Aqdisiyeh, Municipality of District 2 and Golbarg with a concentration of up to 22 μg/m3, had the lowest concentrations (due to higher altitudes than other stations and the probability of having higher wind speed and precipitation rates) and the areas around the stations Shad Abad, Sharif University, Municipality of District 11 and Sadr Highway (up to 36.14 μg/m3), which may be due to the heavy traffic of vehicles from this highway or to carry out bridge repairs had the highest average annual concentration of PM2.5. As described before, regression equations were obtained between AOD and PM2.5 data for each month at each station. Then, by placing the AOD data in the equations, the PM2.5 concentrations were estimated. A good correlation was observed between the mean annual values of measured and estimated PM2.5 of stations with a high correlation coefficient (R2 = 0.99).
Considering the acceptable ability of the regression method to estimate the average annual concentration of particulate matters, it can be used for areas with similar topographical, climatic, stationary and moving pollutant sources (including desert, semi-desert areas and having heavy traffic) conditions. In order to accelerate the prediction (with acceptable accuracy) of the average concentration of PM2.5, the use of satellite remote sensing can be an economical and low cost alternative in comparison to ground pollution monitoring stations, which are usually dispersed and located far from each other.