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Dust storms create some of the most critical air quality problems in the world; the Middle East, located in the dust belt, suffers substantially from dust storms. Iran, as a country in the Middle East, is affected by dust storms from multiple internal and external sources that mostly originate from deserts in Iraq and Syria (especially the Mesopotamia region). To determine the highest dust loadings in the south and west of Iran, dust frequencies were investigated in the eight most polluted stations in the west, southwest, and southern Iran for a period of 21 years from 2000 to 2021. During the study’s duration, the dust frequency was much higher from 2008 to 2012, which coincided with severe droughts reported in Iraq and Syria; from which, we investigated two severe dust storms (as well as the dust sources and weather condition effects) that took place on 15–17 September 2008 and 1–3 June 2012; we used secondary data from ground measurement stations, and satellite and modeling products. In both cases, horizontal visibility was reduced to less than 1 km at most weather stations in Iran. The measured PM10 in the first case reached 834 μg m−3 at Ilam station in west Iran and the Iran–Iraq borders while the measured PM10 in the second case reached 4947 μg m−3 at Bushehr station in the northern shore of the Persian Gulf. The MODIS true color images and MODIS AOD detected the dust mass over Iraq, southern Iran, and Saudi Arabia in both cases; the AOD value reached 4 in the first case and 1.8 in the second case over the Persian Gulf. During these two severe dust storms, low-level jets were observed at 930 hPa atmospheric levels in north Iraq (2008 case) and south Iraq (2012 case). The output of the NAPPS model and CALIPSO satellite images show that the dust rose to higher than 5 km in these dust storm cases, confirming the influence of Shamal wind on the dust storm occurrences.

Missing data in large data analysis has affected further analysis conducted on dataset. To fill in missing data, Nearest Neighbour Method (NNM) and Expectation Maximization (EM) algorithm are the two most widely used methods. Thus, this research aims to compare both methods by imputing missing data of air quality in five monitoring stations (CA0030, CA0039, CA0042, CA0049, CA0050) in Sabah, Malaysia. PM10 (particulate matter with aerodynamic size below 10 microns) dataset in the range from 2003–2007 (Part A) and 2008–2012 (Part B) are used in this research. To make performance evaluation possible, missing data is introduced in the datasets at 5 different levels (5%, 10%, 15%, 25% and 40%). The missing data is imputed by using both NNM and EM algorithm. The performance of both data imputation methods is evaluated using performance indicators (RMSE, MAE, IOA, COD) and regression analysis. Based on performance indicators and regression analysis, NNM performs better compared to EM in imputing data for stations CA0039, CA0042 and CA0049. This may be due to air quality data missing at random (MAR). However, this is not the case for CA0050 and part B of CA0030. This may be due to fluctuation that could not be detected by NNM. Accuracy evaluation using Mean Absolute Percentage Error (MAPE) shows that NNM is more accurate imputation method for most of the cases.

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