Spatio-Temporal Distribution of Particulate Matter (PM2.5) with an Environmental Approach in West and Southwest of Iran Based on SeaWifs, MISR and MODIS Sensors

Document Type : Research Paper


Department of Climatology, Faculty of Earth Sciences, Shahid Beheshti University, Tehran, Iran


The assessment of air pollution has become one of the increasing global concerns and has a significant impact on climate and environment. So far, various strategies for controlling and managing air pollution have been presented, which are widely used in air quality monitoring for air quality measurement indicators. One of the most important indicators in measuring air quality is PM2.5. These particles make up 60 to 70 percent of the total particle and play a significant role in radiative forcing induction through the absorption and diffusion of sunlight and, as it has been said, also greatly reduce the air quality and public health. In recent years, several studies have been carried out based on air quality measurements and PM2.5 observations based on urban pollution. With the launch of satellites and continuous improvement in data retrieval technology, PM2.5-related studies have become more dynamic. The method of data retrieval of PM2.5 based on satellite data during long-term statistical periods, especially for areas where no measured data is available, can be of great help to climate studies and air pollution. The AOD derived from satellite data can be very useful for PM2.5 monitoring, which will be of great importance when developing countries such as Iran are not available in land-based data. Or they do not have a decent time distribution. The precise estimation of PM2.5 involves the application of surface parameters, geographic data and local meteorological data, which leads to the development of more sophisticated models such as multiple regression models and nonlinear models. The method of data retrieval of PM2.5 based on satellite data during long-term statistical periods, especially for areas where no measured data is available, can be of great help to climate studies and air pollution. The aerosols optical depth (AOD), derived from satellite data, can be very useful for monitoring PM2.5. This is important when it is not available in countries with development such as Iran, land data or they do not have a good time distribution. This study, using SeaWIFS, MISR and MODIS data, evaluates the long-term (2016-1998) PM2.5 in the western and southwestern atmosphere of Iran.

Materials and methods
The study area of this study is West and Southwest of Iran including five provinces of Kurdistan, Kermanshah, Hamedan, Ilam and Khuzestan. In this study, PM2.5 hourly data from air pollutants from a collection of stations from the State Environmental Protection Agency was received. To calculate the aerosols optical depth (AOD), three data sources of SeaWfis, MISR and MODIS were used for long-term data. Finally, for calculating the average annual PM2.5 satellite, we used Geographic Weighting Regression (GWR) method and PM2.5 was calculated with spatial resolution of 0.01 arc for west and southwest of Iran. The GWR is a statistical method that allows spatial mapping in predictor coefficients of a relationship (predictive variable of response) based on linear regression and allows us to use the spatial structure of both predictor variables and its coefficients.

Results and discussion
The estimated PM2.5 data validation was calculated using two indices Coefficient of determination (R2) and Root Mean Square Error (RMSE) for Abadan and Ahwaz stations, and the results showed that the PM2.5 value was estimated using data The SeaWifs, MISR and MODIS sensors have good performance in the west and southwest of Iran. Three sensors SeaWifs, MISR and MODIS have shown a similar pattern in the amount of aerosols in the area, so that the Ilam and Khuzestan provinces exhibit maximum AOD, and the widest latitude, the AOD is decreasing. The spatial distribution of PM2.5 concentration during the period from 1998 to 2016 indicated that PM2.5 concentrations in the west and southwest of Iran showed that there was a distinct spatial pattern with strong changes in the whole region. Khuzestan province showed maximum PM2.5 levels between 1998 and 2016, which is significantly higher than other provinces. After the province of Khuzestan, southern parts of the province of Ilam have shown the maximum PM2.5. The average PM2.5 concentration in the west and southwest of Iran is 12.25 μm/m3; the Khuzestan province can be considered a unique region in Iran, due to the high concentration of desert dust transported to this region of the country, the maximum amount of PM2.5 has shown. Minimum AOD value for three sensors was investigated in Khuzestan province, east of Kermanshah province and Lorestan. This significant difference in the amount of aerosols can be due to several demographic variables, complex topography, meteorological factors such as lower wind speed, high relative humidity and precipitation, distances from dust sources, and eventually vegetation. Due to the fact that a large part of the Zagros Range, which has a height of more than 4,000 meters, prevents the influx of dusty aerosols into the area. Also, large areas of Zagros are well covered with vegetation. As a result, complex topography with appropriate vegetation and low population density and significant human inactivity in the mentioned areas are reasons for the lower amount of aerosols compared to the provinces of Ilam and Khuzestan. Trapped desert dust under the meteorological patterns plays a regional role in reducing and increasing PM2.5 in the studied area. Another factor that can greatly influence the variability of suspended particles in the studied area is due to the complexity of the topography and the pathway of many common systems (such as the Sudanese and Mediterranean systems), which is the wetting phenomenon, which can underestimate the precision of the combined and estimated data Impact. The assessment of the location of the cities with the highest increase has shown that most of these cities are located in the border regions or are not far from the border areas of the country. These changes are clearly indicative of the apparent impact of the dust burden on the country and the increase of suspended particles in the atmosphere. Also, the relationship between AOD and PM2.5 can be changed with meteorological parameters such as the depth of the mixture layer, relative humidity, air temperature and wind speed.

The Aerosol optical depth (AOD) in the west and southwest of Iran from south to north and west to east has shown a decrease; the high AOD in the studied area relative to the absorption of Wet deposition, the formation of secondary aerosols and contamination Caused by regional dust storms and human factors (such as combustion of fossil fuels), especially in the areas adjacent to the western borders of Iran, which accumulates particles in these areas due to their proximity to high dust sources. The particles matter in the atmosphere of the provinces located in the west and southwest of Iran have shown that the amount of entrained air entering Iran under dusty storms is the most important factor in the increase of PM2.5 in this region of Iran. The maximum particulate matter were calculated in two provinces of Khuzestan and Ilam, the effect of dust storms has a significant role in this increase. The evaluation of the PM2.5 variation over the course of the period from 1998 to 2016 has shown that between 1998 and 2012, the increasing amount of PM2.5 and subsequently showed a decrease, a change from an inactive diet to a volatile active period. Also, the coefficient of variation of PM2.5 during the studied period showed that more than 30 percent of the particles in the border regions and the route of storm surges to Iran have been. The coefficient of variation of PM2.5 during the studied period showed an increase of more than 30% of the particles in the border regions and the route of storm surges to Iran, so that Saqez, Mehran, Masjed Soleiman, Dehloran and Kermanshah showed the maximum gradient of the computational process.


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