Towards Understanding Variability in Droughts in Response to Extreme Climate Conditions over the Different Agro-Ecological Zones of Pakistan

Dilawar, Adil; Chen, Baozhang; Arshad, Arfan; Guo, Lifeng; Ehsan, Muhammad Irfan; Hussain, Yawar; Kayiranga, Alphonse; Measho, Simon; Zhang, Huifang; Wang, Fei; Sun, Xiaohong; Ge, Mengyu

doi:10.3390/su13126910

Cited by 13 publications

(13 citation statements)

References 74 publications

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“…This phenomenon is frequently observed in modern cities worldwide, contributing to the broader issue of global urban heat islands (UHIs) (Sadik-Zada and Gatto, 2022). The meteorological characteristics of an area undergo degradation due to urbanization, resulting in increasing air temperatures, reducing rainfall, increasing relative humidity, and increasing energy consumption (Arshad et al, 2019;Dilawar et al, 2021a;Dilawar et al, 2021b;Ali et al, 2020;Ali et al 2021;Ali et al 2023;Dilawar et al, 2021c). Riaz et al (2014) revealed that Lahore's urban land area increased from 33.28% (58,977 ha) in 1972 to 55.97% (99,173 ha) in 2009, accompanied by a proportional decrease in green cover during the same timeframe (Riaz et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Evaluating the potential footprints of land use and land cover and climate dynamics on atmospheric pollution in Pakistan

Dilawar,

Chen,

Ul-Haq

et al. 2024

Front. Environ. Sci.

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Evaluating the potential impacts of land use and land cover change (LULCC) and climate change on air pollution is crucial to unravel the driving forces and mechanisms behind changes in air quality. A multi-faceted approach was adopted, including a land change model (LCM) and Mann–Kendall (MK) test, to evaluate the transition of land cover type, changes in climate, and atmospheric pollutants during 2004–2021 in Pakistan. Moreover, a multiscale geographically weighted regression (MGWR) model and a mathematical model were used to assess the potential contribution of LULCC and climate dynamics to atmospheric pollution. It was revealed that during 2004, croplands covered an area of 9.72 × 104 mile2, accounting for 38% of the total area. However, the area of the croplands increased to 10.1 × 104 mile2, accounting for 40% of the total area in 2021. The MK test showed that the north and west–south regions significantly experienced air pollution, with the increasing trend for nitrogen dioxide (NO2) and sulfur dioxide (SO2) being 0.89× 1015 molecules/cm2 per year and 0.54 DU/year, respectively. For climate variability, mean precipitation (Precp) and mean surface pressure (SP) showed a prominent increasing trend, with a maximum value of 1 mm/year and 0.01 Kpa/year, respectively. The mean temperature maximum (Tmax) showed an increasing and decreasing trend, with the highest value of 0.28°C/year and 0.08°C/year, respectively. In the context of contribution, the conversion of cropland to grasslands increased the trend for SO2 concentrations. The highest increasing trend of 1.5 DU for ozone (O3) was found due to conversion of grasslands to shrublands. Additionally, regional climate played a significant role in making air pollution stagnant across the country. Precp and wind speed (WS) contributed significantly in escalating NO2 concentrations in Pakistan, while Precp contributed most (0.004 DU) to increasing SO2 concentrations. For O3, the most influential climate factor was Precp. These results on a long-term temporal scale demonstrated how maintaining climate variability through comprehensive land use management can help improve ambient air quality in Pakistan.

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Section: Introductionmentioning

confidence: 99%

Evaluating the potential footprints of land use and land cover and climate dynamics on atmospheric pollution in Pakistan

Dilawar,

Chen,

Ul-Haq

et al. 2024

Front. Environ. Sci.

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“…Such variability has profound implications for water resource management, agriculture, and overall ecosystem services. Studies have shown that while some regions are experiencing a decrease in precipitation, others may see an increase, further complicating the management strategies required to address these changes [43,44].…”

Section: Study Areamentioning

confidence: 99%

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Ejaz,

Khan,

Shahid

et al. 2024

Water

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Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.

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“…Aerosol optical depth retrievals from multiple satellite algorithms were calibrated based on Aerosol Robotic Network (AERONET) observations (https://sedac.ciesin.columbia.edu (10 September 2022)) [63,64]. Three meteorological parameters (temperature maximum, temperature minimum, and precipitation) were obtained from the Fifth generation (ERA5) (https://www.ecmwf.int/en, accessed on 28 September 2022) [82][83][84] as in the description written for Table 1. Ground measurements were used to compare the satellite-estimated and NASA SEDAC data at some pollution monitoring stations as different locations as indicated in Figure 1.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Meanwhile, the MK test assumes that time series have no sequential correlation and is based on the assumption that all data are significant [88]. These techniques are widely used to analyze climatic parameters in particular [82,89].…”

Section: Trend Analysismentioning

confidence: 99%

Investigating the Potential Climatic Effects of Atmospheric Pollution across China under the National Clean Air Action Plan

et al. 2023

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To reduce air pollution, China adopted rigorous control mechanisms and announced the Air Pollution Prevention and Control Action Plan (APPCAP) in 2013. Here, using OMI satellite, the NASA Socioeconomic Data and Application Center (SEDAC), and Fifth ECMWF (ERA5) data at a 0.25° × 0.25° resolution, we explored changes in NO2, PM, SO2, and O3 and climatology over China in response to the Action Plan between 2004 and 2021. This study attempts to investigate the long term trend analysis of air pollution and climatic variations during two scenarios before (2004–2013) and after (2013–2021) APPCAP. We investigated the climatic effects of air pollution in China before and after APPCAP adoption using geographically weighted regression (GWR) and differential models to assess the contribution of air pollution. The spatial representation analysis demonstrated how air pollution affected climatic factors before and after the APPCAP. Several important findings were derived: (1) the APPCAP significantly influenced air pollution reduction in China post-scenario (2013–2021); (2) the Mann Kendall test investigated that all pollutants showed an increasing trend pre-APPCAP, while they showed a decreasing trend, except for O3, post-APPCAP; (3) for climatic factors, the MK test showed an increasing trend of precipitation and mean minimum air temperature tmin post-APPCAP; (4) innovative trend analysis (ITA) showed a reduction in NO2, SO2, and PM, although O3 showed no trend post-APPCAP; and (5) pre-scenario, NO2 contributed to an increase in the mean maximum air temperature (tmax) by 0.62 °C, PM contributed to raising tmin by 0.41 °C, while O3 reduced the tmax(tmin) by 0.15 °C (0.05 °C). PM increased tmax and precipitation with a magnitude 0.38 °C (7.38 mm), and NO2 contributed to increasing tmin by (0.35 °C), respectively, post-scenario. In particular, post-scenario led to an increase in tmin and precipitation across China. The results and discussion presented in this study can be beneficial for policymakers in China to establish long-term management plans for air pollution and climatological changes.

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Towards Understanding Variability in Droughts in Response to Extreme Climate Conditions over the Different Agro-Ecological Zones of Pakistan

Cited by 13 publications

References 74 publications

Evaluating the potential footprints of land use and land cover and climate dynamics on atmospheric pollution in Pakistan

Evaluating the potential footprints of land use and land cover and climate dynamics on atmospheric pollution in Pakistan

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Investigating the Potential Climatic Effects of Atmospheric Pollution across China under the National Clean Air Action Plan

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