PM10 distribution using remotely sensed data and GIS techniques; Klang Valley, Malaysia

Remote sensing and GIS have been increasingly used for air pollution monitoring in past decade. In this study the distribution of PM10 were measured at eight air quality monitoring stations in Klang Valley. The attempt was carried out in GIS environment. The data are belonging to the beginning of the week –Monday- and weekend –Saturday-. Aerosol optical thickness (AOT) values retrieved from Moderate Resolution Imaging Spectroradiometer (MODIS) were interpolated in GIS for comparison with ground station PM10 data. The validation between AOT and amount of PM10 in the atmosphere were analyzed using non-linear correlation coefficient (NLCC) for 2004. Results showed that the amount of PM10 at the beginning of the week is higher than the weekend. Remote sensing data showed better distribution of PM10 than ground station data. The NLCC results had a range from (0.10) at Petaling Jaya to (0.61) at Shah Alam. This study shows that GIS is useful tool to generate distribution map of PM10. This study shows that MODIS AOT data are able to present the amount of PM10 over large spatial scales that there is no ground stations air quality monitoring

Assessment of Vegetation Variation on Primarily Creation Zones of the Dust Storms Around the Euphrates Using Remote Sensing Images

Recently, period frequency and effect domain of the dust storms that enter Iran from Iraq have increased. In this study, in addition to detecting the creation zones of the dust storms, the effect of vegetation cover variation on their creation was investigated using remote sensing. Moderate resolution image Spectroradiometer (MODIS) and Landsat Thematic Mapper (TM5) have been utilized to identify the primarily creation zones of the dust storms and to assess the vegetation cover variation, respectively. Vegetation cover variation was studied using Normalized Differences Vegetation Index (NDVI) obtained from band 3 and band 4 of the Landsate satellite. The results showed that the surrounding area of the Euphrates in Syria, the desert in the vicinity of this river in Iraq, including the deserts of Alanbar Province, and the north deserts of Saudi Arabia are the primarily creation zones of the dust storms entering west and south west of Iran. The results of NDVI showed that excluding the deserts in the border of Syria and Iraq, the area with very weak vegetation cover have increased between 2.44% and 20.65% from 1991 to 2009. In the meanwhile, the retention pound surface areas in the south deserts of Syria as well as the deserts in its border with Iraq have decreased 6320 and 4397 hectares, respectively. As it can be concluded from the findings, one of the main environmental parameters initiating these dust storms is the decrease in the vegetation cover in their primarily creation zones

Land surface temperature assessment in semi-arid residential area of Tehran, Iran using Landsat imagery

Land cover change especially from green areas to urban areas may increase land surface temperature (LST). In this study, Landsat Enhanced Thematic Mapper Plus (ETM+) on 15 May 2000 (spring), 9 July 2000 (summer), 26 November 2000 (autumn) and 10 January 2001(winter) were utilized to study LST in Tehran, Iran. The accuracy of the LST analysis was evaluated using six year ground temperature data. The Non Linear Correlation Coefficient (NLCC) between normalized differences vegetation index (NDVI) and LST was found to be higher in the spring compared to the other seasons. The LST value in the west of the city was similar to the surrounding areas, but in north, east and south of the city were lower compared to the north, north east and east of the surrounding areas in all seasons. The gravel and sandy soil in the western part of the surrounding areas were warmer than the impervious surface area (ISA) in the city in summer. It was found that high urban density in semi arid climate with low vegetation in the surrounding areas does not increase the LST value in the city compared to its surrounding areas

Impact assessment of climate change in Iran using LARS-WG model

Iran is situated in a very diverse environmental area. The climate of the region is varied and influenced by different patterns. In order to best describe the expected climate change impacts for the region, climate change scenarios and climate variables must be developed on a regional, or even site-specific, scale. The weather generator is one of the valid downscaling methods. In the current study, LARS- WG (a weather generator) and the outputs from ECHO-G for present climate, as well as future time slice of 2010-2039 based on A1 scenario, were used to evaluate LARS-WG as a tool at 13 synoptic stations located in the north and northeast parts of Iran. The results obtained in this study illustrate that LARS-WG has a reasonable capability of simulating the minimum and maximum temperatures and precipitation. In addition, the results showed that the mean precipitation decreased in Semnan, the south of Khorasan and Golestan. Meanwhile, the mean temperature during 2010-2039 would increase by 0.5°C, especially in the cold season

PM10 monitoring using MODIS AOT and GIS, Kuala Lumpur, Malaysia.

Remote sensing has been increasingly used in retrieval Aerosol optical thickness (AOT) to particulate matter pollution monitoring. In this study, Moderate resolution image Spectroradiometer (MODIS) data were utilized in particulate matter pollution monitoring. Daily aerosol optical thickness (AOT) data retrieved from MODIS using Non-Linear Correlation Coefficient (NLCC) with polynomial equation Were compared with the amount of particulate matter PMIO measured at Three ground Air Quality Monitoring Stations (AQMS)-Victoria Kl, Cheras Kl and Gombak- in Kuala lumpur and surrounding area. The PMIO data were imported in geographical information system (GIS) environment to derive the PMIO maps in Kuala Lumpur stations. Results showed that the amounts of PMIO in dry season are higher than those in rainy season in stations. The NLCC between MODIS AOT and PMIO concentration was obtained higher in Victoria Kl compared to Gombak and Cheras Kl. GIS maps were found to show better distribution of PMIO compared to the ground station data. This study reveals AOT data from MODIS and GIS map can be utilized to study the air quality, especially distribution of PMIO in the places where there are ground measurements

Estimating solar radiation using NOAA/AVHRR and ground measurement data

Solar radiation (SR) data are commonly used in different areas of renewable energy research. Researchers are often compelled to predict SR at ground stations for areas with no proper equipment. The objective of this study was to test the accuracy of the artificial neural network (ANN) and multiple linear regression (MLR) models for estimating monthly average SR over Kurdistan Province, Iran. Input data of the models were two data series with similar longitude, latitude, altitude, and month (number of months) data, but there were differences between the monthly mean temperatures in the first data series obtained from AVHRR sensor of NOAA satellite (DS1) and in the second data series measured at ground stations (DS2). In order to retrieve land surface temperature (LST) from AVHRR sensor, emissivity of the area was considered and for that purpose normalized vegetation difference index (NDVI) calculated from channels 1 and 2 of AVHRR sensor was utilized. The acquired results showed that the ANN model with DS1 data input with R2 = 0.96, RMSE = 1.04, MAE = 1.1 in the training phase and R2 = 0.96, RMSE = 1.06, MAE = 1.15 in the testing phase achieved more satisfactory performance compared with MLR model. It can be concluded that ANN model with remote sensing data has the potential to predict SR in locations with no ground measurement stations

Real time assessment of haze and PM10 aided by MODIS aerosol optical thickness over Klang Valley, Malaysia

Scarcely distribution, installation and maintenance costs for ground monitoring stations are issues in air pollution monitoring. Moderate resolution imaging Spectroradiometer (MODIS) on board of Terra and Aqua satellites is able to retrieve aerosol optical thickness (AOT) in troposphere and can be utilized in particulate matter pollution monitoring. In this study, daily AOT data retrieved from MODIS in 2004 to 2006, using Non-linear correlation coefficient (NLCC) were compared with the amount of particulate matter PM10 measured at eight ground air quality monitoring stations in Klang Valley, Malaysia. Effects of haze on air quality that indicated MODIS AOT before, during and after the severe haze were also studied. Results showed that the air quality conditions in dry season are unhealthy and correlation coefficients between MODIS AOT and PM concentration are higher than those in rainy season. The corresponding AOT change during the rainy 10 season was between lower than 0.1 and 2.5. It shows that for the rainy season it is less than 0.1. This study reveals AOT data from MODIS can be utilized to study the air quality, especially PM in the places where there 10 are not any ground measurements

Identification of Canebrake level changes of the Zarivar Lake between 1984 to 2011, using the images of Landsat TM and ETM +

In the past decade, remote sensing has been widely used to identify surface changes of different vegetation and their classification. Increasing the level of Canebrake of the Zarivar Lake and its risks for aquatic organisms living in the lake has become one of the most important issues in recent years. Therefore, the aim of this study was to identify surface changes of this Canebrake in the past three decades using Landsat TM and ETM+. For this purpose, bands 3, 4, and 5 of images were geo-referenced. RMSE were less than one pixel for all bands. The supervised classification method with a maximum likelihood algorithm was also applied to detect the changes of water area on the combined images (bands 5, 4, and 3) of months with full water in the lake. NDVI index was utilized to identify the surface changes of Canebrake on the images taken in the months with low water in the lake. The results show that the rise and fall of water area and surrounding canebrake has a direct correlation with a rainfall and increase in both levels maybe occur at the same time. Study on the coastal strip of water area with GPS and combined images showed that the coastal line had not a significant change in the past three decades

Urban heat evolution in a tropical area utilising landsat imagery

Cloud cover is the main limitation of using remote sensing to study Land Use and Land Cover (LULC) change, and Land Surface Temperature (LST) in tropical area like Malaysia. In order to study LULC change and its effect on LST, the Landsat images were utilized within Geographical Information System (GIS) with the aim of removing the effect of cloud cover and image's gaps on the Digital Number (DN) of the pixels. 5356 points according to pixels coordinate which represent the 960 m to 960 m area were created in GIS environment and matched with thermal bands of the study area in remote sensing environment. The DNs of these points were processed to extract LST and imported in GIS environment to derive the temperature maps. Temperature was found to be generally higher in 2010 than in 2000. The comparison of the highest temperature area in the temperature maps with ground stations data showed that the topographical characteristics of the area, and the wind speed, and direction influence the occurrence of Urban Heat Island (UHI) effect. This study concludes that integration of remote sensing data and GIS is a useful tool in urban LST detection in tropical area