Chemical and source characterization of PM2.5 in summertime in severely polluted Lahore, Pakistan Publication date: April 2020 Source: Atmospheric Research, Volume 234 Author(s): Mushtaq Ahmad, Siming Cheng, Qing Yu, Weihua Qin, Yuepeng Zhang, Jing Chen Abstract Lahore is considered the most polluted city in Pakistan with high levels of atmospheric particulate matter. The PM2.5 chemical composition and sources in Lahore were investigated in this study in an effort to effectively mitigate the pollution of PM2.5 in Lahore. In the month of July 2018, the day and night samples of PM2.5 in Lahore were collected and analyzed. Principal component analysis (PCA), concentration weighted trajectory (CWT) calculation and analysis of the spatial distribution of black carbon in Lahore obtained from MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, Version 2) reanalysis data were employed to identify the sources and contributing regions of PM2.5. The fuel consumption situation in Pakistan was also examined to further identify the major contributing sectors to air pollution. The most abundant species in the identified mass of PM2.5 were carbonaceous compounds, followed by ionic species such as SO42−, NO3−, Cl− and NH4+. Although secondary organic carbon showed a high percentage in OC (47%), primary organic carbon was still dominant in OC in Lahore. PM2.5 showed no correlation with meteorological parameters. Based on the CWT calculation and black carbon spatial distribution, local emission sources were the main sources of air pollution in Lahore, with an additional contribution from the western states of India. Overall, primary emissions from local sources showed a dominant contribution to PM2.5 pollution and controlling the emissions from industry, transportation, and power plants would show immediate effects for mitigating PM2.5 in Lahore. Capsule Comprehensive analyses of PM2.5 concentration and composition, meteorological conditions, PM2.5 sources, and consumption status of fossil fuel demonstrated the affecting factors of PM2.5 pollution in Lahore, Pakistan.

Termination of thunderstorm-related bursts of energetic radiation and particles by inverted intracloud and hybrid lightning discharges Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): A. Chilingarian, Y. Khanikyants, V.A. Rakov, S. Soghomonyan Abstract Recent studies of thunderstorm-related enhancements of fluxes of energetic radiation and particles at ground level suggest that removal of mid-level negative charge from the cloud by negative cloud-to-ground (-CG) lightning flashes or normal intracloud (IC) flashes serves to abruptly terminate those enhancements. However, it was not clear if the electron-accelerating electric field responsible for flux enhancements at ground was primarily between the main negative charge region and ground (produced due to the dominant effect of negative cloud charge) or between the mid-level negative and lower positive charge regions inside the thundercloud. Here, we report that these flux enhancements can be also abruptly terminated by inverted intracloud flashes and hybrid lightning flashes (inverted IC followed by negative CG). Based on the analysis of 13 events of these two types, we provide first evidence that the electric field between the mid-level negative and lower positive charge regions in the thundercloud can be responsible for the flux enhancements at ground level.

On the connection between large-scale atmospheric circulation and winter GPCP precipitation over the Mediterranean region for the period 1980-2017 Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): G. Kotsias, C.J. Lolis, N. Hatzianastassiou, V. Levizzani, A. Bartzokas Abstract The influence of the large-scale atmospheric circulation on winter precipitation over the Mediterranean region is studied with the use of monthly 2.5° × 2.5° ERA-Interim 500 and 1000 hPa geopotential heights and GPCP-v2.3 precipitation, for the period 1980–2017. At first, Principal Component Analysis (PCA) is applied both to geopotential heights and precipitation datasets and the main modes of inter-annual variations are found. Then, these modes are investigated through Canonical Correlation Analysis (CCA) to identify the centers of action of the lower and the middle troposphere that control precipitation variability over the Mediterranean region. Three statistically significant canonical pairs are found. The first one, in the North Atlantic, controls precipitation over western Asia Minor, northern Libya and the western Iberian Peninsula. Τhe second mode corresponds to the see-saw between Greenland and central Europe and controls precipitation over the northern Mediterranean region. The third one is located north of Britain and controls precipitation over NW Africa and northern France. For each canonical pair, the mean geopotential heights and precipitation anomaly patterns for the four (10%) years with the highest and the lowest canonical scores are constructed, confirming the results of CCA. Moreover, the correlation between the canonical scores and the indices of the large-scale atmospheric oscillations, namely the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the North Sea – Caspian Pattern (NCP) and the East Atlantic/West Russia Pattern (EA/WR), was studied.

Physical-empirical models for prediction of seasonal rainfall extremes of Peninsular Malaysia Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Sahar Hadi Pour, Ahmad Khairi Abd Wahab, Shamsuddin Shahid Abstract Reliable prediction of rainfall extremes is vital for disaster management, particularly in the context of increasing rainfall extremes due to global climate change. Physical-empirical models have been developed in this study using three widely used Machine Learning (ML) methods namely, Support Vector Machines (SVM), Random Forests (RF), Bayesian Artificial Neural Networks (BANN) for the prediction of rainfall and rainfall related extremes during Northeast Monsoon (NEM) in Peninsular Malaysia from synoptic predictors. The gridded daily rainfall data of Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) was used to estimate four rainfall indices namely, rainfall amount, average rainfall intensity, days having >95-th percentile rainfall, and total number of dry days in Peninsular Malaysia during NEM for the period 1951–2015. The National Centers for Environmental Prediction (NCEP) reanalysis sea level pressure (SLP) data was used for the prediction of rainfall indices with different lead periods. The recursive feature elimination (RFE) method was used to select the SLP at different NCEP grid points which were found significantly correlated with NEM rainfall indices. The results showed superior performance of BANN among the ML models with normalised root mean square error of 0.04–0.14, Nash-Sutcliff Efficiency of 0.98–1.0, and modified agreement index of 0.97–0.99 and Kling-Gupta efficient index 0.65–0.96 for one-month lead period prediction. The 95% confidence interval (CI) band for BANN was found narrower than the other ML models. Almost all the forecasted values by BANN were also found with 95% CI, and therefore, the p-factor and the r-factor for BANN in predicting rainfall indices were found in the range of 0.95–1.0 and 0.25–0.49 respectively. Application of BANN in prediction of rainfall indices with higher lead time was also found excellent. The synoptic pattern revealed that SLP over the north of South China Sea is the major driver of NEM rainfall and rainfall extremes in Peninsular Malaysia.

Absorption characteristics of aerosols over the central Himalayas and its adjacent foothills Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Hema Joshi, Manish Naja, Liji M. David, Tarun Gupta, Mukunda M. Gogoi, S. Suresh Babu Abstract The absorption characteristics and source processes of aerosols are investigated at two nearby distinct altitude sites: Nainital, located over the central Himalayas (~1958 m amsl) and Pantnagar, in the adjacent foothill region (~231 m amsl) in the Indo-Gangetic Plain region (IGP); based on in-situ measurements and model (GEOS-Chem) simulations. The study reveals the significant influence of biomass burning sources over both the locations during spring, indicating the efficiency of the vertical transport of biomass burning aerosols during the peak of the fire activity period over the northern Indian region. On the other hand, the dominance of fossil fuel emission sources is seen during most part of the year at the mountain site, while biomass/biofuel sources are prevalent at the foothill site. Simulations of different aerosol components in the GEOS-Chem model have revealed that dust aerosols, in addition to carbonaceous aerosols from fossil fuel and biomass burning sources, significantly influence aerosol burden over this broad region covering both high-altitude site Nainital and adjacent foothill site Pantnagar in IGP. Examination of dominant aerosol types and their contribution to the columnar abundance of aerosols is performed. During spring, the contribution of dust aerosols is as high as 22%, even though inorganic aerosols (42%), organic carbon (29%) play a dominant role in modulating aerosol absorption characteristics in the column over this region. This study highlights the importance of absorbing aerosol, their types and quantification for better estimates of radiative forcing of aerosols over this region. This might also provide valuable information for the regional impact assessment of aerosols over the Himalayan region.

Characteristics and formation mechanisms of secondary inorganic ions in PM2.5 during winter in a central city of China: Based on a high time resolution data Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Liuming Yang, Shenbo Wang, Shiguang Duan, Qishe Yan, Nan Jiang, Ruiqin Zhang, Shengli Li Abstract This study aimed to investigate the characteristics of the water-soluble ions concentrations in atmospheric particulates. Highly time-resolved measurements of inorganic ions associated with PM2.5 were conducted from December 1, 2017 to February 28, 2018 in Zhengzhou. The hourly mean and standard deviation of PM2.5 concentration during the observation episodes were 108.2 ± 80.7 μg/m3. The hourly mass concentration of PM2.5 increased from 8 μg/m3 to 438 μg/m3 throughout the entire observation. The proportion of water-soluble inorganic ions in PM2.5 was 52.5% throughout the entire observation period. The ions existed mainly in the form of (NH4)2SO4 and NH4NO3. The average mass concentration ratio of NO3− to SO42− was 1.9 ± 0.8 throughout the entire observation period, which initially increased and then decreased with the increased pollution level. The average ratio of the molar equivalent concentration of [NH4+] to that of [NO3− + SO42−] was 1.14 ± 0.27, which decreased with the increased pollution level. Homogeneous reactions played an important role in the formation of nitrate, while, the heterogeneous reactions were important in the formation of sulfate. Both of the values of sulfur oxidation ratios (SOR) and nitrogen oxidation ratios increased with relative high humidity (RH) condition; especially, the SOR values sharply increased when the RH was above 50%. The results of potential source contribution function model demonstrated that the western and northeastern regions of Zhengzhou had a greater influence on PM2.5 pollution in Zhengzhou. All these results suggested that reducing the emission of precursors of secondary inorganic ions was highly important in controlling PM2.5 pollution in Zhengzhou.

Synergistic regulation of the interdecadal variability in summer precipitation over the Tianshan mountains by sea surface temperature anomalies in the high-latitude Northwest Atlantic Ocean and the Mediterranean Sea Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Xiaoyuan Yue, Ge Liu, Junming Chen, Changyan Zhou Abstract We explored the interdecadal variability in summer precipitation over the Tianshan mountains and the associated background of atmospheric circulation anomalies. There was a clear interdecadal increase in summer precipitation over the Tianshan mountains during the time period 1961–2016, with lower precipitation from 1961 to 1990 and higher precipitation from 1993 to 2016. The sea surface temperature anomalies in the high-latitude northwest Atlantic and the Mediterranean Sea are partly responsible for the interdecadal variability of precipitation in the Tianshan mountains. The warmer sea surface temperature anomalies in these two regions excite a positive anomaly of geopotential height over the northwest Atlantic and to the north of the Mediterranean Sea, respectively, synergistically modulating the downstream atmospheric circulation near Lake Baikal via the eastward dispersion of Rossby wave energy originating from the northwest Atlantic via the Black Sea. As a result, an interdecadal increase in geopotential height has occurred near Lake Baikal since the 1990s. Corresponding to this interdecadal increase in geopotential height, an anomalous anticyclone appeared near Lake Baikal during 1993–2016, inducing an anomalous easterly flow over the Tianshan mountains. This anomalous easterly flow caused an interdecadal decrease in the eastward export of water vapor on the eastern boundary of the Tianshan mountain region, resulting in an interdecadal increase in the total water vapor budget over the mountains. This has facilitated the interdecadal increase in summer precipitation over the Tianshan mountains since the 1990s.

Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Hamid Gholami, Aliakbar Mohamadifar, Adrian L. Collins Abstract Spatial modelling of storm dust provenance is essential to mitigate its on-site and off-site effects in the arid and semi-arid environments of the world. Therefore, the main aim of this study was to apply eight data mining algorithms including random forest (RF), support vector machine (SVM), bayesian additive regression trees (BART), radial basis function (RBF), extreme gradient boosting (XGBoost), regression tree analysis (RTA), Cubist model and boosted regression trees (BRT) and an ensemble modelling (EM) approach for generating spatial maps of dust provenance in the Khuzestan province, a main region with active sources for producing dust in southwestern Iran. This study is the first attempt at predicting storm dust provenance by applying individual data mining models and ensemble modelling. We identified and mapped in a geographic information system (GIS), 12 potential effective factors for dust emissions comprising two for climate (wind speed, precipitation), five soil characteristics (texture, bulk density, Ec, organic matter (OM), available water capacity (AWC)), a normalized difference vegetation index (NDVI), land use, geology, a digital elevation model (DEM) and land type, and used a mean decrease accuracy measure (MDAM) to determine the corresponding importance scores (IS). A multicollinearity test (including the variance inflation factor (VIF) and tolerance coefficient (TC)) was applied to assess relationships between the effective factors, and an existing map of dust provenance was randomly categorized into two groups consisting of training (70%) and validation (30%) data. The individual data mining models were validated using the area under the curve (AUC). Based on the TC and VIF results, no collinearity was detected among the 12 effective factors for dust emissions. The prediction accuracies of the eight data mining models and an EM assessed by the AUC were as follows: EM (with AUC = 99.8%) > XGBoost > RBF > Cubist > RF > BART > SVM > BRT > RTA (with AUC = 79.1%). Among all models, the EM was found to provide the highest accuracy for predicting storm dust provenance. Using the EM, areas classified as being low, moderate, high and very high susceptibility for storm dust provenance comprised 36, 13, 23 and 28% of the total mapped area, respectively. Based on MDAM results, the highest and lowest IS were obtained for the wind speed (IS = 23) and geology (IS = 6.5) factors, respectively. Overall, the modelling techniques used in this research are helpful for predicting storm dust provenance and thereby targeting mitigation. Therefore, we recommend applying data mining EM approaches to the spatial mapping of storm dust provenance worldwide. Graphical abstract

Evolutionary drought patterns over the Sahel and their teleconnections with low frequency climate oscillations Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Christopher E. Ndehedehe, Nathan O. Agutu, Vagner G. Ferreira, Augusto Getirana Abstract The need for ideal drought metrics to explore the impacts of climate variability drivers on drought intensity and characteristics is essential to provide support that leads to optimizing existing templates on risk mitigation in drought-prone regions. The main aim of this study therefore is to improve contemporary understanding on the evolutionary patterns of historical drought over the Sahel (1901–2014) and the large-scale processes that drive its variability using the SPEI (standardized precipitation evapotranspiration index) and SPI (standardized precipitation index). Historically, the distributions of SPEI and SPI are generally quite similar with fairly strong correlations ranging from 0.80 to 1.0 around the central Sahel. The dissimilarity between SPI and SPEI in some regions however, suggest the influence of changing land surface conditions and the need to incorporate biophysical indicators to support the accounting process that aid the understanding of drought impacts and its cascading effects. Furthermore, SPEI and SPI confirm that the worst drought during the 20th century over the Sahel occurred between 1982 and 1985, affecting more than 90% of the region while the 1950s remained the wettest on record. However, between 2005 and 2015, drought episodes and their intensities have diminished over the Sahel. In the assessment of the coupled relationship of seven climate modes with drought evolutions, SPEI was more associated with Atlantic Multi-decadal Oscillation-AMO (r = 0.66) and Pacific Decadal Oscillation (r = − 0.53) unlike the SPI. A predictive multivariate model confirms the AMO is a strong driver of drought events and in addition, highlights the multi-scale climate influence in the Sahel.

Selection of GCMs for the projection of spatial distribution of heat waves in Pakistan Publication date: 1 March 2020 Source: Atmospheric Research, Volume 233 Author(s): Najeebullah Khan, Shamsuddin Shahid, Kamal Ahmed, Xiaojun Wang, Rawshan Ali, Tarmizi Ismail, Nadeem Nawaz Abstract Performance of 31 General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 5 (CMIP5) was assessed according to their ability to reconstruct the different properties of heat waves (HWs); HW frequency, HW duration and HW index estimated using Princeton Global Meteorological Forcing (PGF) daily temperature data for the period 1961 to 2005 in order to generate an ensemble for the projection of HWs in Pakistan. The GCMs were selected based on three criteria: (1) ability to replicate the decadal variability in HW properties, (2) ability to reconstruct the spatial distribution of HW properties based on Taylor skill score, (3) replicate the annual time series of HW properties based on standard statistical indices and compromise programming. Results revealed four GCMs: CCSM4, CESM1(BGC), CMCC-CM and NorESM1-M are the most suitable for the projection of HWs over Pakistan. Projection of HWs using the selected GCMs revealed increase in the frequency and severity of HWs in most parts of Pakistan for both the radiative concentration pathway (RCP4.5 and RCP8.5) scenarios used in the study. The frequency of HWs was projected to increase up to 12 events per year while the duration was projected to increase up to 100 days in a year during 2060 to 2099 for the highest emission scenario. Overall, the HWs were projected to be more frequent and longer duration in the east and the southern coastal regions.