Since its first appearance in Wuhan, China at the end of 2019, the new coronavirus (COVID-19) has evolved a global pandemic within three months, with more than 4.3 million confirmed cases worldwide ...until mid-May 2020. As many countries around the world, Malaysia and other southeast Asian (SEA) countries have also enforced lockdown at different degrees to contain the spread of the disease, which has brought some positive effects on natural environment. Therefore, evaluating the reduction in anthropogenic emissions due to COVID-19 and the related governmental measures to restrict its expansion is crucial to assess its impacts on air pollution and economic growth. In this study, we used aerosol optical depth (AOD) observations from Himawari-8 satellite, along with tropospheric NO2 column density from Aura-OMI over SEA, and ground-based pollution measurements at several stations across Malaysia, in order to quantify the changes in aerosol and air pollutants associated with the general shutdown of anthropogenic and industrial activities due to COVID-19. The lockdown has led to a notable decrease in AOD over SEA and in the pollution outflow over the oceanic regions, while a significant decrease (27% - 30%) in tropospheric NO2 was observed over areas not affected by seasonal biomass burning. Especially in Malaysia, PM10, PM2.5, NO2, SO2, and CO concentrations have been decreased by 26–31%, 23–32%, 63–64%, 9–20%, and 25–31%, respectively, in the urban areas during the lockdown phase, compared to the same periods in 2018 and 2019. Notable reductions are also seen at industrial, suburban and rural sites across the country. Quantifying the reductions in major and health harmful air pollutants is crucial for health-related research and for air-quality and climate-change studies.
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•Impact of lockdown due to COVID-19 on aerosols and pollutants over Southeast Asia•Reduction in Himawari-8 AOD at urban areas is not affected by seasonal biomass burning•Large reductions (~27% - 34%) of tropospheric NO2 over urban agglomerations•Reductions in PM10, PM2.5, NO2, SO2, and CO are 26-31%, 23-32%, 63-64%, 9-20%, and 25-31%, respectively, in Malaysia (urban)
The lockdown measures implemented worldwide to slow the spread of the COVID–19 pandemic have allowed for a unique real-world experiment, regarding the impacts of drastic emission cutbacks on urban ...air quality. In this study we assess the effects of a 7-week (23 March–10 May 2020) lockdown in the Greater Area of Athens, coupling in situ observations with estimations from a meteorology-atmospheric chemistry model. Measurements in central Athens during the lockdown were compared with levels during the pre- and post-lockdown 3-week periods and with respective levels in the four previous years. We examined regulatory pollutants as well as CO2, black carbon (BC) and source-specific BC components. Models were run for pre-lockdown and lockdown periods, under baseline and reduced-emissions scenarios. The in-situ results indicate mean concentration reductions of 30–35% for traffic-related pollutants in Athens (NO2, CO, BC from fossil fuel combustion), compared to the pre-lockdown period. A large reduction (53%) was observed also for the urban CO2 enhancement while the reduction for PM2.5 was subtler (18%). Significant reductions were also observed when comparing the 2020 lockdown period with past years. However, levels rebounded immediately following the lift of the general lockdown. The decrease in measured NO2 concentrations was reproduced by the implementation of the city scale model, under a realistic reduced-emissions scenario for the lockdown period, anchored at a 46% decline of road transport activity. The model permitted the assessment of air quality improvements on a spatial scale, indicating that NO2 mean concentration reductions in areas of the Athens basin reached up to 50%. The findings suggest a potential for local traffic management strategies to reduce ambient exposure and to minimize exceedances of air quality standards for primary pollutants.
Dust storms represent a major environmental challenge in the Middle East. The southwest part of Iran is highly affected by dust events transported from neighboring desert regions, mostly from the ...Iraqi plains and Saudi Arabia, as well as from local dust storms. This study analyzes the spatio-temporal distribution of dust days at five meteorological stations located in southwestern Iran covering a period of 22 years (from 1997 to 2018). Dust codes (06, 07, 30 to 35) from meteorological observations are analyzed at each station, indicating that 84% of the dust events are not of local origin. The average number of dust days maximizes in June and July (188 and 193, respectively), while the dust activity weakens after August. The dust events exhibit large inter-annual variability, with statistically significant increasing trends in all of five stations. Spatial distributions of the aerosol optical depth (AOD), dust loading, and surface dust concentrations from a moderate resolution imaging spectroradiometer (MODIS) and Modern-Era Retrospective analysis for Research and Applications (MERRA-2) retrievals reveal high dust accumulation over southwest Iran and surrounding regions. Furthermore, the spatial distribution of the (MODIS)-AOD trend (%) over southwest Iran indicates a large spatial heterogeneity during 2000–2018 with trends ranging mostly between −9% and 9% (not statistically significant). 2009 was the most active dust year, followed by 2011 and 2008, due to prolonged drought conditions in the fertile crescent and the enhanced dust emissions in the Iraqi plains during this period. In these years, the AOD was much higher than the 19-year average (2000 to 2018), while July 2009 was the dustiest month with about 25–30 dust days in each station. The years with highest dust activity were associated with less precipitation, negative anomalies of the vegetation health index (VHI) and normalized difference vegetation index (NDVI) over the Iraqi plains and southwest Iran, and favorable meteorological dynamics triggering stronger winds.
Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on ...deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model’s output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model’s predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model’s output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.
Total suspended particulates (TSP), as a key pollutant, is a serious threat for air quality, climate, ecosystems and human health. Therefore, measurements, prediction and forecasting of TSP ...concentrations are necessary to mitigate their negative effects. This study applies the gated recurrent unit (GRU) deep learning model to predict TSP concentrations in Zabol, Iran, during the dust period of the 120-day wind (3 June - 4 October 2014). Three uncertainty quantification (UQ) techniques consisting of the blackbox metamodel, heteroscedastic regression and infinitesimal jackknife were applied to quantify the uncertainty associated with GRU model. Permutation feature importance measure (PFIM), based on the game theory, was employed for the interpretability of the predictive model's outputs. A total of 80 TSP samples were collected and were randomly divided as training (70%) and validation (30%) datasets, while eight variables were used in the TSP prediction model. Our findings showed that GRU performed very well for TSP prediction (with r and Nash Sutcliffe coefficient (NSC) values above 0.99 for both datasets, and RMSE of 57 μg m−3 and 73 μg m−3 for training and validation datasets, respectively). Among the three UQ techniques, the infinitesimal jackknife was the most accurate one, while all the observed and predicted TSP values fell within the continence limitation estimated by the model. PFIM plots showed that wind speed and air humidity were the most and least important variables, respectively, impacting the predictive model's outputs. This is the first attempt of using an interpretable DL model for TSP prediction modelling, recommending that future research should involve aspects of uncertainty and interpretability of the predictive models. Overall, UQ and interpretability techniques have a key role in reducing the impact of uncertainties during optimization and decision making, resulting in better understanding of sophisticated mechanisms related to the predictive model.
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•We quantified the uncertainty associated with TSP predicted by GRU model.•PFIM was employed for the interpretability of the predictive model's output.•Among the three UQ techniques, the infinitesimal jackknife was the most accurate.•The PFIM plots showed that wind speed is the most important variables.
Assessment of indoor air quality is especially important, since people spend substantial amounts of time indoors, either at home or at work. This study analyzes concentrations of selected heavy ...metals in 40 indoor dust samples obtained from houses in the highly-industrialized Asaluyeh city, south Iran in spring and summer seasons (20 samples each). Furthermore, the health risk due to exposure to indoor air pollution is investigated for both children and adults, in a city with several oil refineries and petrochemical industries. The chemical analysis revealed that in both seasons the concentrations of heavy metals followed the order of Cr > Ni > Pb > As > Co > Cd. A significant difference was observed in the concentrations of potential toxic elements (PTEs) such as Cr, As and Ni, since the mean (±stdev) summer levels were at 60.2 ± 9.1 mg kg−1, 5.6 ± 2.7 mg kg−1 and 16.4 ± 1.9 mg kg−1, respectively, while the concentrations were significantly lower in spring (17.6 ± 9.7 mg kg−1, 3.0 ± 1.7 mg kg−1 and 13.5 ± 2.4 mg kg−1 for Cr, As and Ni, respectively). Although the hazard index (HI) values, which denote the possibility of non-carcinogenic risk due to exposure to household heavy metals, were generally low for both children and adults (HI < 1), the carcinogenic risks of arsenic and chromium were found to be above the safe limit of 1 × 10−4 for children through the ingestion pathway, indicating a high cancer risk due to household dust in Asaluyeh, especially in summer.
Toxic heavy metals in rainwater samples of Tehran Malekei, Roholah; Sayadi, Mohammad Hossein; Behrooz, Reza Dahmardeh ...
Journal of atmospheric chemistry,
12/2024, Letnik:
81, Številka:
1
Journal Article
Recenzirano
This study investigates the concentrations and spatial distribution of toxic heavy metals (Cd, Cu, Pb and Zn) through chemical analysis of rainwater samples collected in Tehran, Iran during winter ...and spring of 2022, characterized by different land use, emission sources, traffic conditions and population density. The average concentrations of the examined heavy metals at the five sampling sites were 52.9, 11.8, 14.6 and 0.93 μg l
−1
for Zn, Pb, Cu and Cd, respectively. The concentrations of all heavy metals were significantly higher (
p
< 0.05) at the sampling points in central and south Tehran compared to sites in the west and north, due to different urban characteristics, higher pollution emission rates from the traffic and domestic sectors, and local wind patterns developed within the city. High traffic load in the central part of Tehran also escalates the heavy metal concentrations in this region. The significant correlations between the examined heavy metals at the five sites indicate common, local anthropogenic sources. The heavy metal concentrations were higher for rain samples collected in spring than in winter, likely associated with dilution processes in winter and the restriction measures due to COVID-19 pandemic. During the lockdown period, a drastic decrease in traffic load was observed in Tehran, confirming that motor vehicles is the main regulatory factor for air pollution and potential toxic elements in the city.
High aerosol levels pose severe air pollution and climate change challenges in Iran. Although regional aerosol optical depth (AOD) trends have been analyzed during the dusty season over Iran, the ...specific factors that are driving the spatio-temporal variations in winter AOD and the influence of meteorological dynamics on winter AOD trends remain unclear. This study analyzes the long-term AOD trends over Iran in winter during the period 2000–2020 using the updated Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA-2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) datasets. Our results showed that the winter AOD exhibited a significant upward trend during the period 2000–2010 followed by a significant decrease during the period 2010–2018. We found that the winter AOD trends are important over this arid region due to multiple meteorological mechanisms which also affect the following spring/summer dusty period. Ground-based observations from Aerosol Robotic Network data (AERONET) in the Middle East region display trends comparable to those of both MERRA-2 and MODIS and indicated that aeolian dust and the meteorological dynamics associated with it play a central role in winter AOD changes. Furthermore, this study indicated that a significant downward trend in winter sea level pressure (SLP) during the early period (2000–2010) induced hot and dry winds which originated in the desert regions in Iraq and Arabia and blew toward Iran, reducing relative humidity (RH) and raising the temperature and thus promoting soil drying and dust AOD accumulation. In contrast, a significant increase in winter SLP during the late period (2010–2018) induced cold and wet winds from northwestern regions which increased RH and lowered the temperature, thus reducing dust AOD. This suggests that the changes in AOD over Iran are highly influenced by seasonal meteorological variabilities. These results also highlight the importance of examining wintertime climatic variations and their effects on the dust aerosol changes over the Middle East.
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•Analysis of the vegetation trends for different land cover types.•Determination of areas suitable for future restoration projects.•Reforestation, sand dunes and clay pits are the ...most favorable for restoration projects.•Assessment of the restoration areas according to topography and climatic type.
This study examines the trends in vegetation cover using the Growing Season NDVI (GSN) time series in moderate spatial resolution (250 m) over Khorasan Razavi province, in northeast Iran, during the period 2004–2015. The province is largely desert, with extra-arid, arid, and semi-arid de Martonne climate zones dominating, while rangelands, shrublands and deserts cover most areas, making it an ideal territory for monitoring vegetation trends and implement future restoration projects. Most parts of the province and land-cover classes show no trends in vegetation cover, but large decreasing trends occur in areas covered by sand dunes, previously reforested lands and clay pit areas. Trends in various land-cover types are also examined as functions of the climatic class and the Terrain Niche index (TNI), which is characteristic of the topography, revealing large decreasing trends in the extra-arid climatic zone. In addition, most of the areas exhibit Hurst exponent values around 0.5, implying stochastic time series without any consistency and a likelihood of random vegetation and land cover changes in the future. This study also aims to determine likely future vegetation status and the most favourable areas for restoration projects through analysis of two indexes (Future Restoration Dispersal Index, FRDI and Future Uncertainty Dispersal Index, FUDI). The results show that reforestation, sand dunes and clay pits areas are the most favourable for implementing restoration projects, while the spatial distribution of the potential restoration classes reveals that the southern and northeastern parts of Khorasan Razavi province are the most favourable areas for establishing environmental restoration activities in order to avoid further degradation of ecosystems.
This study analyzes six frontal dust storms in the Middle East during the cold period (October–March), aiming to examine the atmospheric circulation patterns and force dynamics that triggered the ...fronts and the associated (pre- or post-frontal) dust storms. Cold troughs mostly located over Turkey, Syria and north Iraq played a major role in the front propagation at the surface, while cyclonic conditions and strong winds facilitated the dust storms. The presence of an upper-atmosphere (300 hPa) sub-tropical jet stream traversing from Egypt to Iran constitutes also a dynamic force accompanying the frontal dust storms. Moderate-Resolution Imaging Spectroradiometer (MODIS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) observations are used to monitor the spatial and vertical extent of the dust storms, while model (Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), Copernicus Atmospheric Monitoring Service (CAMS), Regional Climate Model-4 (RegCM4)) simulations are also analyzed. The WRF-Chem outputs were in better agreement with the MODIS observations compared to those of CAMS and RegCM4. The fronts were identified by WRF-Chem simulations via gradients in the potential temperature and sudden changes of wind direction in vertical cross-sections. Overall, the uncertainties in the simulations and the remarkable differences between the model outputs indicate that modelling of dust storms in the Middle East is really challenging due to the complex terrain, incorrect representation of the dust sources and soil/surface characteristics, and uncertainties in simulating the wind speed/direction and meteorological dynamics. Given the potential threat by dust storms, more attention should be directed to the dust model development in this region.
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