The vertically distributed aerosol optical properties are investigated over Pakistan utilizing the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 products from 2007 to 2014. For a better understanding of the spatiotemporal characteristics of vertical aerosol layers, the interannual and seasonal variations of nine selected aerosol parameters such as the AOD of the lowest aerosol layer (AOD_L), the base height of the lowest aerosol layer (H_L), the top height of the highest aerosol layer (H_H), the volume depolarization ratio of the lowest aerosol layer (DR_L), the color ratio of the lowest aerosol layer (CR_L), total AOD of all the aerosol layers (AOD_T), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (T_L), the AOD proportion for the lowest aerosol layer (PAOD_L) for both day and night times are analyzed. The results show AOD_T increased slightly from 2007 to 2014 over Pakistan, and relatively high AOD_T exists over the Punjab and Sindh (southern region), which might be owing to the high level of economic development, frequent dust storms, and profound agricultural activities (anthropogenic emissions). AOD_T increases from north to south. The reason may be that the southern region is rapidly urbanized and is near the desert. The northern region is dominated by agricultural land, and cities are usually semi-urbanized. The highest AOD_T appears in summer compared to the other seasons, and during daytime compared to nighttime. The H_L and H_H vary significantly, owing to the topography of Pakistan. The N is relatively large over Punjab and Sindh compared to the other regions, which might be caused by relatively stronger atmospheric convections. The spatial distribution of the T_L showed an inverse relationship with the topography as lower values are observed over elevated regions such as Gilgit-Baltistan and Jammu-Kashmir. The value of the PAOD_L indicates that 77% of the total aerosols are mainly concentrated in the lowest layer of the atmosphere over Pakistan. The higher values of DR_L and CR_L indicate non-spherical and large particles over Balochistan and Sindh, which might be related to the proximity to the desert. This study provides very useful information about vertically distributed aerosol optical properties which could help researchers and policymakers to regulate and mitigate air pollution issues of Pakistan.

The optical and physical characteristics of the aerosol vertical layers over Northeastern China (NEC) are investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) Level 2 layer products from 2007 to 2014. To better examine the spatial and temporal variations in the characteristics of aerosols over NEC, the region is divided into three parts (Heilongjiang province, Jilin province, and Liaoning province) to analyze the inter-annual and seasonal variations of nine selected aerosol parameters in each part during night and day times. The results reveal that the values of aerosol optical depth (AOD) increase year by year, over the whole NEC, being relatively high over the Liaoning (LN) province; this might be induced by higher levels of economic development and agricultural activity. The highest AOD values appear in summer, which is plausibly related to the temperate monsoon climate in NEC. Higher AOD values exist during the daytime than at night; this is intuitively the result of higher daytime anthropogenic activities. The base altitude of the lowest aerosol layer (BAL) and the top altitude of the highest aerosol layer (TAH) varied significantly due to the topography of NEC. The number of aerosol layers (N) is relatively large over LN, which might be caused by a relatively stronger atmospheric convection over this landscape. The thickness of the lowest aerosol layer (TLL) bore little relationship with the topography of NEC. The AOD proportion of the lowest aerosol layer (PAODL) is high (0.70 to 0.85 for the entire NEC), indicating that aerosols are mainly concentrated in the lowest layer of the atmosphere. The volume depolarization ratio of the lowest aerosol layer (VDRL) is large during spring and winter due to the presence of dust aerosols. The color ratio of the lowest aerosol layer (CRL) is large during the day due to relatively more human activities taking place than at night. Moreover, there is a significantly positive linear correlation between N and TAH, and a negative logarithm correlation between N and PAODL over NEC. The results of this study could provide researchers and the government departments with detailed and certain optical and physical information about aerosol layers over NEC, to help in the treatment of air pollution over NEC.

In this study, spatio-temporal characteristics of particulate matter (PMx; x = 2.5 and 10) mass concentrations and aerosol optical properties were analyzed over the water source area of the South–North Water Diversion Central Line. For this purpose, PM_2.5 and PM₁₀ mass concentrations were collected at the Taocha(TC)station from October 2018 to September 2019, and aerosol optical depth (AOD) was obtained from the Cloud-Aerosol LiDAR and Infrared Pathfinder Satellite Observation (CALIPSO) satellite from 2007 to 2019. The monthly, seasonal, and daily statistical analyses and related comparisons were conducted in the present study. The results showed that the PM₁₀ concentrations meet China’s ambient air secondary quality standard (100 μg/m³ annual mean), whereas PM_2.5 did not meet China’s ambient air secondary quality standard (35 μg/m³ annual mean) at the TC station, no obvious seasonal and diurnal variations are observed, and these particulates are caused by local emissions and outside sources. A significant positive correlation of PM_2.5 and PM₁₀ was observed with relative humidity and temperature, whereas no relationship was found with wind direction. The results also showed low (~0.1) AOD in spring, autumn, and winter, whereas slightly higher AOD (~0.3) was observed in summer. This may be caused by straw burning from long-distance transportation. This study may provide new data support for comprehensive ecological measures such as strengthening the ecological environment and water quality protection in the Middle Route Project of the South–North Water Diversion.

The optical characteristics of vertically distributed aerosols over Saudi Arabia were investigated using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data from 2007 to 2019. The study region was divided into three parts (Region I: Tabuk, Makkah, Al Madinah, Asir, Al Bahah, Jizan, Riyadh, Mecca, Medina, the eastern region, Kassim, Hale, Asir, Baha, Tabuk, the northern border region, Jizan, Najilan, and Jufu. Region II: Ar, Al, Ha, Al, and Najran. Region III Al Hudud ash Shamaliyah and Ash Sharqiyah) to understand regional aerosol characteristics by performing interannual and seasonal analysis for nine aerosol types during the day and nighttime. We found that the aerosol optical depth (AOD) estimates were the highest over eastern Saudi Arabia (region III) and were seemingly driven by the presence of an expansive desert in the region. As anticipated, the AOD observations were substantially higher in spring and summer than in autumn and winter owing to the frequent occurrence of dust events during the former. Daytime observations exhibited higher AOD values than those at nighttime, which might be related to higher daytime anthropogenic activities. The estimates of the base height of the lowest aerosol layer (HB₁) and the top altitude of the highest aerosol layer (TAH) were altered depending on the topography (the higher the altitude, the higher the annual mean value of HB₁ and TAH). The aerosol layers (N) were relatively abundant over region III, seemingly due to the relatively stronger atmospheric convection over this region. The volume depolarization ratio of the lowest aerosol layer (VDR₁) was considerable during the night due to deposition at nighttime, and VDR₁ was relatively substantial in spring and summer. The color ratio of the lowest aerosol layer (CR₁) estimates over regions II and III was higher at night. We report a weak positive correlation between the thickness of the lowest aerosol layer (HTH₁) and the AOD of the lowest aerosol layer (AOD₁) in the three regions, a strong positive correlation between TAH and N, and a negative correlation between the AOD proportion of the lowest aerosol layer (PAOD₁) and N in Saudi Arabia. In this paper, the optical and physical properties of aerosols in Saudi Arabia have been studied for 13 years. Our results could provide references for researchers and the government, and relevant departments with data support on the aerosol layer to help control air pollution in Saudi Arabia.

Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R² = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (R_o) and air temperature (T)) SS empirical model (R² = 0.80). Similarly. the two-input ANN models (maximum R² = 0.83) also generally performed better than the two-input (R_o and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.