Assessment of drought using different tests and drought indices in Elazig, Turkey.

Water is one of the most essential elements for human life and must be provided for life requirements. Historical changes in meteorological data are vital for the planning and operation of water management. A total of 516-time series were used to evaluate the characteristics of drought in Elazig in Turkey. In this study, meteorological drought analysis was carried out in monthly and annual periods by using the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Precipitation Index (SPI, Innovative Polygon Trend Analysis (IPTA), and China-Z Index (CZI) drought indices. As a result, it was determined that there was an increase in dry periods for all time scales for eight meteorological stations, especially in 2000 and after. A downward trend was detected in precipitation data, while an upward trend was detected in temperature and evaporation data based on a 95% confidence interval. Although normal drought has the highest share among drought categories, very severe drought has the lowest share. it is determined that SPI gives more sensitive results in the very severe drought category than the SPEI index. As a result, the region's trend of rain and temperature will assist water management for resource planning.

Assessment of meteorological parameters on air pollution variability over Delhi.

In this study, the relationships between meteorological parameters (relative humidity, wind speed, temperature, planetary boundary layer, and rainfall) and air pollutants (particulate matter and gaseous pollutants) have been evaluated during a 3-year period from 2019 to 2021. Diffusion and dispersion of air contaminants were significantly influenced by meteorology over the capital city. The results of correlation matrix and principal component analysis (PCA) suggest a season's specific influence of meteorological parameters on atmospheric pollutants' concentration. Temperature has the strongest negative impact on pollutants' concentration, and all the other studied meteorological parameters negatively (reduced) as well as positively (increased) impacted the air pollutants' concentration. A two-way process was involved during the interaction of pollutants with relative humidity and wind speed. Due to enhanced moisture-holding capacity during non-monsoon summers, particles get larger and settle down on the ground via dry deposition processes. Winter's decreased moisture-holding capacity causes water vapour coupled with air contaminants to remain suspended and further deteriorate the quality of the air. High wind speed helps in the dispersion and dilution but a high wind speed associated with dust particles may increase the pollutants' level downwind side. The PM2.5/PM10 variation revealed that the accumulation effect of relative humidity on PM2.5 was more intense than PM10. Daily average location-specific rainfall data revealed that moderate to high rainfall has a potential wet scavenging impact on both particulate matters and gaseous pollutants.

Assessment of economic, energy, and exergy efficiencies using wind measurement mast data for different wind turbines.

Today, increasing concerns about greenhouse gas emissions, climate change, and resource depletion from fossil fuels have drawn attention to wind energy. In this context, wind turbine technologies are constantly evolving to eliminate such concerns by using wind energy. The wind speed from the measurement mast at a height of 80 m was used in wind turbines of different capacities and was investigated. To assess the potential of the system that produces electricity from wind energy, it has been analyzed in terms of energy, exergy, and economic. The energy and exergy efficiencies of each wind turbine were analyzed with the wind speed and meteorological data. When the average monthly power calculated for each turbine is proportioned to the turbine capacity, the energy efficiency varies between 10 and 70%. Enercon_1500 and Enercon_3050 values are high, while Enercon_3500 and Enercon_2350 have low efficiency compared to other turbines. The annual total energy production is 12.19 GWh for the highest Enercon_4200 and 4.48 GWh for the lowest Enercon_1500. The exergy efficiencies range from 20 to 79% for selected wind turbines. In the last part of the study, monthly average electricity production costs were determined by using the turbines selected for the determined region. When compared in terms of unit electricity cost, the Enercon_1500 turbine is higher, while the Enercon_4200 is lower.

Accuracy assessment and climatology of MODIS aerosol optical properties over North Africa.

In this study, the aerosol optical depth (AOD) from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) product was compared with ground-based measurements at five sites of the Aerosol Robotic Network (AERONET) in North Africa. The MODIS AOD showed a good correlation coefficient of ~0.78, a very small mean bias error of 0.009, and a root mean square error of 0.126 with AERONET. The Dark Target/Deep Blue (DT/DB) algorithm showed better performance at low aerosol loading while underestimating AOD at higher aerosol loading, mainly for coarse-dominated aerosol types. This work also showed the benefits of using MODIS retrievals as a reliable data source for aerosols and providing a long-term aerosol type classification. The primary aerosol type is dust emitted from the Sahara Desert, and the dusty atmosphere becomes gradually mixed with pollution aerosols approaching the coastal region. The annual mean MODIS AOD at 550 nm and Ångström exponent at 412-650 nm (AE) ranged from 0.17 to 0.45 and from 0.13 to 1.25, respectively, in Algeria between 2001 and 2019. Lower AOD (< 0.22) and higher AE (> 1) were found in the northern region, while the highest AOD (0.35 to 0.45) and the lowest AE (< 0.25) were observed over the Tanezrouft desert in southern Algeria. The seasonal mean AOD was highest in summer, while the lowest was in winter due to very high easterly and northeasterly Harmattan surface wind over Zone of Chotts and the Tidikelt Depression, respectively. The negative AOD trends observed over Algeria could be partially connected to the decline (increase) in surface (850 hPa) winds over potential dust source areas in southern Algeria.

Multi-scale analysis of the impacts of meteorology and emissions on PM2.5 and O3 trends at various regions in China from 2013 to 2020 3. Mechanism assessment of O3 trends by a model.

A multiscale analysis of meteorological trends was carried out to investigate the impacts of the large-scale circulation types as well as the local-scale key weather elements on the complex air pollutants, i.e., PM2.5 and O3 in China. Following accompanying papers on synoptic circulation impact and key weather elements and emission contributions (Gong et al., 2022a; Gong et al., 2022b), an emission-driven Observation-based Box Model (e-OBM) was developed to study the impact mechanisms on O3 trend and quantitatively assess the effects of variation in the emissions control over 2013-2020 for Beijing, Chengdu, Guangzhou and Shanghai. Compared with the original OBM, the e-OBM not only improves the performance to simulate the hourly O3 peak concentration in daytime, but also reasonably reproduces the maximum daily 8-hour average (MDA8) O3 concentrations in the four cities. Based upon the sensitivity experiments, it is found that the meteorology is the dominant driver for the MDA8 O3 trend, contributing from about 32 % to 139 % to the variations. From the mechanistic point of view, the variations of meteorology lead to the enhancement of atmospheric oxidation capacity and the acceleration of O3 production. Further evaluation to the emission changes in four cities shows that the O3-precursors relationships of the four cities have been changed from the VOC-limited regime in 2013 to the transition regime or near-transition regime in 2020. Though the NOx/VOCs ratios have been obviously decreased, the emission reductions up to 2020 were still not enough to mitigate O3 pollution in these cities. It is emphasized in this study that the strengthened control measures with maintaining a certain ratio of NOx and VOCs should be implemented to further curb the increasing trend of O3 in urban areas.

Variability of ambient black carbon concentration in the Central Himalaya and its assessment over the Hindu Kush Himalayan region.

During 2015-2018, eight black carbon (BC) monitoring sites were established in Nepal and Bhutan to fill a significant data gap regarding BC measurement in Central Himalaya. This manuscript analyzes and presents data from these eight stations and one additional station on the Tibetan plateau (TP). Complex topography, varied emission sources, and atmospheric transport pathways significantly impacted the BC concentrations across these stations, with annual mean concentrations varying from 36 ng m-3 to 45,737 ng m-3. Higher annual mean concentrations (5609 ± 4515 ng m-3) were recorded at low-altitude sites than in other locations, with seasonal concentrations highest in the winter (7316 ± 2541 ng m-3). In contrast, the annual mean concentrations were lowest at high-altitude sites (376 ± 448 ng m-3); the BC concentrations at these sites peaked during the pre-monsoon season (930 ± 685 ng m-3). Potential source contributions to the total observed BC were analyzed using the absorption angstrom exponent (AAE). AAE analysis showed the dominance of biomass burning sources (>50 %), except in Kathmandu. By combining our data with previously published literature, we put our measurements in perspective by presenting a comprehensive assessment of BC concentrations and their variability over the Hindu Kush Himalayan (HKH) region. The BC levels in all three geographic regions, high, mid, and low altitude significantly influenced by the persistent seasonal meteorology. However, the mid-altitude stations were substantially affected by valley dynamics and urbanization. The low-altitude stations experienced high BC concentrations during the winter and post-monsoon seasons. Concentration weighted trajectory (CWT) and frequency analyses revealed the dominance of long-range transported pollution during winter over HKH, from west to east. South Asian sources remained significant during the monsoon season. During pre- and post-monsoon, the local, regional, and long-distance pollution varied depending on the location of the receptor site.

Spatio-temporal drought assessment of the Subarnarekha River basin, India, using CHIRPS-derived hydrometeorological indices.

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann-Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40 years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1 month (SPI1) and 3 months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins.

Assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, India.

Drought is considered among the most perilous events with catastrophic consequences, particularly from the agro-economic point of view. These consequences are expected to exacerbate under the increasing meteorological aberrations due to changing climate, which necessitates investigating drought variabilities. This study presents a thorough spatiotemporal assessment of drought trends and variabilities over the agriculture-dominated Marathwada Region, Maharashtra, India. The precipitation data is extracted from the India Meteorological Department (IMD) gridded product, whereas actual evapotranspiration (ET) and Evaporative Stress Index (ESI) are obtained from Global Land Evaporation Amsterdam Model (GLEAM) datasets. Standardized Precipitation Index (SPI) is used to characterize drought occurrences at multiple time frames, whereas non-parametric tests, i.e., modified Mann-Kendall (MMK) and Sen's slope (SS) tests, are employed to detect trends. The results reveal the region to be prone to droughts, and SPI at a longer time frame (i.e., 12-monthly moving frame) can capture drought occurrences better than the shorter time frames, which can be attributed to the lesser randomness in the time series in the longer frame. A mix of positive/negative trends of SPI series are found for the monsoonal months; however, they are relatively more concentrated towards negative ZMMK. Hence, the Marathwada Region can be inferred to have exhibited a relatively increased tendency towards drought occurrences. The seasonal differences in mean values and trends of rainfall, ET, and ESI are discussed in detail. Since the Marathwada Region has a monsoon-dominated climate with high agricultural importance, the information reported in this study will help in devising water management strategies to minimize the repercussions of droughts.

Drought characterization over Indian sub-continent using GRACE-based indices.

Drought is a natural disaster affects water resources, agriculture, and social and economic development due to its long-term and frequent occurrence. It is crucial to characterize and monitor drought and its propagation to minimize the impact. However, spatiotemporal assessment of drought characteristics over India at the sub-basin scale based on terrestrial water storage is unexplored. In this study, the Terrestrial water storage anomalies (TWSA) obtained from a Gravity Recovery and Climate Experiment and precipitation data are used to characterize the propagation of drought. Combined Climatological Deviation Index (CCDI) and GRACE-Drought Severity Index (GRACE-DSI) were computed as CCDI utilizes both precipitation and TWSA data while GRACE-DSI uses only TWSA data. Our results showed that GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to depletion of TWS. While other sub-basins show changing trends for GRACE-DSI and CCDI. The number of sub-basins showing significant negative trends for GRACE-DSI is more than that for CCDI. Hence TWS is depleting for most of the subbasins in India. Our results show that Indo-Gangetic plains face many drought events during 2002-2004, 2009-2014 & 2015-2017. Maximum drought duration and drought severity obtained for the area of North Ladakh (not draining into Indus basins) by GRACE-DSI are 26 months (2002-2004) and - 44.2835, respectively. The maximum drought duration and drought severity obtained for the Shyok sub-basin by CCDI is 17 months (2013-2015) and - 13.4392, respectively. Monthly trend analysis revealed that 39 & 23 no. of sub-basins show significant negative GRACE-DSI trends for October and CCDI for November, respectively. At the same time, the seasonal trend shows that total 34 and 14 sub-basins exhibited a significant negative trend at post-monsoon Kharif season for both the GRACE-DSI & CCDI, respectively.

Drought Assessment Based on Data Fusion and Deep Learning.

Drought is a major factor affecting the sustainable development of society and the economy. Research on drought assessment is of great significance for formulating drought emergency policies and drought risk early warning and enhancing the ability to withstand drought risks. Taking the Yellow River Basin as the object, this paper utilizes data fusion, copula function, entropy theory, and deep learning, fuses the features of meteorological drought and hydrological drought into a drought assessment index, and establishes a long short-term memory (LSTM) network for drought assessment, based on deep learning theory. The results show that (1) after extracting the features of meteorological drought and hydrological drought, the drought convergence index (DCI) built on the fused features by copula function can accurately reflect the start and duration of the drought; (2) the drought assessment indices were effectively screened by judging the causality of the drought system, using the transfer entropy; (3) drawing on the idea of deep learning, LSTM for drought assessment, which was established on DCI and the drought assessment factors, can accurately assess the drought risks of the Yellow River Basin.

Assessing and mapping human well-being for sustainable development amid drought and flood hazards: Dadu River Basin of China.

Drought and flood are two of the most destructive natural disasters with the most significant impact and greatest losses in the Dadu River basin (DRB). However, their impacts on people's life have not attracted enough attention from scholars. In this study, the Standardized Precipitation Index (SPI) describing the drought/flood situation and the Composite Index of Human Well-being (CIHW) are calculated, and a framework is further constructed to assess the impacts of drought and flood disasters on human well-being in the DRB. The results show that the annual and seasonal SPI in the DRB generally exhibit an increasing trend in fluctuations during 2000-2009, indicating a wetting climate in this basin. Overall, the upper reaches of the DRB have experienced an evolution of flood-drought-flood state transition, where the variation amplitude of the SPI in the western sub-basin is greater than that in the eastern sub-basin. In addition, the lower reaches of the DRB have suffered more dramatic and periodic changes from the drought/flood disasters in terms of the SPI. For human well-being during 2000-2019, Maerkang City in the upper reaches, Kangding City in the middle reaches, and Shimian County in the lower reaches of the DRB are at a relatively higher level, with the CIHW decreasing from administrative centers to the around. Moreover, the CIHW over the whole basin increases gradually from 2000 to 2019. The SPI has significantly negative effects on different capitals, following a descending order of financial, social, physical, human and natural capitals. The counties of the basin are divided into four groups, namely the group with high disaster risks and high human well-being, the group with high disaster risks and low human well-being, the group with low disaster risks and high human well-being, and the group with low disaster risks and low human well-being. The panel regression results suggest that the construction of water conservancy facilities, the financial inputs in agriculture and meteorology, and the educational level have positive impacts on human well-being, but the impacts differ from different groups. The construction of water conservancy facilities has highly significant impacts on human well-being in all groups; the education level has no significant impact on the group with high disaster risk and high human well-being, which has not passed the significance test; while the financial inputs in agriculture and meteorology have relatively higher impacts on the whole basin and on the group with low disaster risk and low human well-being compared with other groups. Therefore, it is suggested that the negative impacts of drought and flood disasters can be mitigated through strengthening infrastructure construction, responding appropriately to climate change, avoiding disasters at the source of major projects and improving the disaster prevention and mitigation systems.

Thermodynamic assessment of heat stress in dairy cattle: lessons from human biometeorology.

A versatile meteorological index for predicting heat stress in dairy cattle remains elusive. Despite numerous attempts at developing such indices and widespread use of some, there is growing skepticism about the accuracy and adequacy of the existing indices as well as the general statistical approach used to develop them. At the same time, precision farming of high-yielding animals in a drastically changing climate calls for more effective prediction and alleviation of heat stress. The present paper revisits classical work on human biometeorology, particularly the apparent temperature scale, to draw inspiration for advancing research on heat stress in dairy cattle. The importance of a detailed, mechanistic understanding of heat transfer and thermoregulation is demonstrated and reiterated. A model from the literature is used to construct a framework for identifying and characterizing conditions of potential heat stress. New parameters are proposed to translate the heat flux calculations based on heat-balance models into more tangible and more useful meteorological indices, including an apparent temperature for cattle and a thermoregulatory exhaustion index. A validation gap in the literature is identified as the main hindrance to the further development and deployment of heat-balance models. Recommendations are presented for systematically addressing this gap in particular and continuing research within the proposed framework in general.

Drought variability assessment using standardized precipitation index, reconnaissance drought index and precipitation deciles across Balochistan, Pakistan.

Drought variability analysis is of utmost concern for planning and efficiently managing water resources and food security in any specific area. In the current study, drought spell occurrence has been investigated in the Balochistan province of Pakistan during the past four decades (1981-2020) using standardized precipitation index (SPI), reconnaissance drought index (RDI), and precipitation deciles (PD) at an annual timescale. Precipitation and temperature data collected from 13 synoptic meteorological stations located in Balochistan were used to calculate the SPI, the RDI, and the PD for calculation of drought severity and duration. Based on these indices, temporal analysis shows adverse impacts of drought spells in Nokkundi during 1991-1993, in Barkhan, Dalbandin, Quetta stations during 1999-2000, whereas Barkhan, Dalbandin, Lasbella, Sibi during 2002-2003, Zhob during 2010-2011, Kalat and Khuzdar during 2014-2015, and Panjgur during 2017-2018. Also, the aridity index for each station was calculated based on the UNEP method shows that major part of Balochistan lies in the arid zone, followed by the hyper-arid in the southwestern part and the semi-arid zones in the northeastern part of the province. SPI and RDI results were found more localized than PD, as PD shows extensive events. Furthermore, principal component analysis shows a significant contribution from all the indices. For SPI, RDI, and PD, the first three principal components have more than 70% share, contributing 73.63%, 74.15%, and 72.30% respectively. By integrating drought patterns, long-term planning, and preparedness to mitigate drought impacts are only possible. The RDI was found more suitable and recommended in case of temperature data availability.

Case of study on a sustainability building: Environmental risk assessment related with allergenicity from air quality considering meteorological and urban green infrastructure data on BIM.

Digitisation is gaining importance with 3D workflow for architecture-specific annotation of built heritage. The objective is to use the Building Information Modeling (BIM) methodology in order to carry out a study of alternatives of impact on environmental sustainability associated with the potential allergenicity with green infrastructure on a new housing, located in Mérida (SW Spain). It is intended to simulate the meteorology (direction and speed of the wind) in the study city with the compass rose for 18 years (2003-2020) to assess the meteorological pattern associated with the wind on the studied housing. 3 green infrastructure garden alternatives (considering 5 ornamental species of cypress trees) were designed to evaluate the potential impact of allergenicity on the housing. AIROT index was applied to project the results on the frontage of the housing. This index was developed in the field of large areas of urban environments. The calculation was carried out in the most exact way possible in specific sections of the frontage of the housing and automatically with tools associated with the BIM environment (such as Autodesk Revit, Dynamo, Enscape, Wrplot and Bim One) to the discipline of Architecture (such as Autodesk Autocad and Autodesk Flow Design). The obtained results were applied to evaluate 3 scenario designs, trying to minimize the potential exposure to urban green infrastructure (focus on cypress trees) in this current project, and offering a health reference guide in future projects, from the design phase considering appropriate measures and proposing recommendations.

Development of an assessment framework for the proposed Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI) for regional drought classifications in global warming context.

Drought hazard is one of the main consequences of global warming and climate change. Unlike other natural disasters, drought has complex climatic features. Therefore, accurate drought monitoring is a challenging task. This paper proposes a framework for assessing drought classifications at the regional level. The proposed framework provides a new drought monitoring indicator called Multi-Scalar Seasonally Amalgamated Regional Standardized Precipitation Evapotranspiration Index (MSARSPEI). MSARSPEI is an amalgam of the Standardized Precipitation Evapotranspiration (SPEI) (Vicente-Serrano et al., 2010) and Regionally Improved Weighted Standardized Drought Index (RIWSDI) (Jiang et al., 2020). In the proposed framework, the Boruta algorithm of feature selection is configured to ensemble monthly time series data of evaporation in various meteorological stations located in specific regions. Further, the framework suggests the standardization of the Cumulative Distribution Function (CDF) of K-Component Gaussian (K-CG) mixture distribution function for obtaining MSARSPEI data. The application of the proposed framework is based on seven different regions of Pakistan. For comparative analysis, this paper compared the performance of MSARSPE with SPEI using Pearson correlation. Outcomes associated with this research show that the proposed regional drought index has a strong correlation with the competing indicator in various time scales. In addition, the study assessed the spatial extent of various drought classifications under MSARSPEI. In summation, this research concludes that the choice of the MSARSPEI is rationally valid and more appropriate for the regional assessment of drought under the global warming scenario.

Improved Monitoring and Assessment of Meteorological Drought Based on Multi-Source Fused Precipitation Data.

Meteorological drought, one of the most frequent climate-related disasters, causes great danger for human health and socioeconomic development. With an aim to improve the accuracy of meteorological drought monitoring, this study collected multi-source remotely-sensed precipitation products, i.e., the Tropical Rainfall Measuring Mission (TRMM), the Global Precipitation Measurement Mission (GPM), and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), and compared their performance over Hubei Province, China. The geographic difference analysis was used to blend the best-fitted product with gauged precipitation data. Based on the fused dataset with verification, the spatio-temporal characteristics of drought were investigated. Results showed that GPM performed the best in precipitation numerical evaluation and event detection with a 5 mm/d threshold. The fused data accurately captured 80% of historical drought events and indicated that extreme annual droughts mainly occurred in the northern and northwestern regions, while slight, moderate, and severe droughts mainly occurred in the central and eastern parts. The short-term drought exhibited the highest frequency of 33% in summer and the lowest frequency of 27% in spring, while the medium-term drought showed a higher frequency in autumn and winter. This could be a preliminary assessment of drought based on multi-source fused precipitation data for precise drought outlook and risk management.

Spatiotemporal assessment of aeromycoflora under differing urban green space, sampling height, and meteorological regimes: the atmospheric fungiscape of Thessaloniki, Greece.

We studied the diversity and abundance of the airborne fungal spores in the city of Thessaloniki, Greece, for two consecutive years. Air samples were collected at one rooftop station (at 30 m) and six near-ground stations (at 1.5 m) that differed in the size and composition of adjacent green spaces. The effects of meteorological factors on airborne fungal spore concentrations were also explored. Cladosporium spores were dominant everywhere in the air of the city. The total concentration of the airborne fungal spores at 30 m was 10 times lower than near the ground. Differences in concentration and composition were far less pronounced among near-ground stations. The attributes of the fungal spore season did not change in a consistent way among stations and years. Concentrations at the near-ground stations matched the grouping of the latter into stations of high, intermediate, and low urban green space. Minimum air temperature was the primary meteorological factor affecting spore abundance, followed by relative humidity. Airborne fungal spores are more homogeneously distributed in the air of the city, but their concentrations decrease more rapidly with height than pollen.

Assessment of health benefit of PM2.5 reduction during COVID-19 lockdown in China and separating contributions from anthropogenic emissions and meteorology.

The national lockdown policies have drastically disrupted socioeconomic activities during the COVID-19 pandemic in China, which provides a unique opportunity to investigate the air quality response to such anthropogenic disruptions. And it is meaningful to evaluate the potential health impacts of air quality changes during the lockdown, especially for PM2.5 with adverse health effects. In this study, by using PM2.5 observations from 1388 monitoring stations nationwide in China, we examine the PM2.5 variations between the COVID-19 lockdown (February and March in 2020) and the same period in 2015-2019, and find that the national average of PM2.5 decreases by 18 µg/m3, and mean PM2.5 for most sites (about 75%) decrease by 30%-60%. The anthropogenic and meteorological contributions to these PM2.5 variations are also determined by using a stepwise multiple linear regression (MLR) model combined with the Kolmogorov-Zurbenko filter. Our results show that the change of anthropogenic emissions is a leading contributor to those widespread PM2.5 reductions, and meteorological conditions have the negative influence on PM2.5 reductions for some regions, such as Beijing-Tianjin-Hebei (BTH). Additionally, the avoided premature death due to PM2.5 reduction is estimated as a predicted number based on a log-linear concentration-response function. The total avoided premature death is 9952 in China, with dominant contribution (94%) from anthropogenic emission changes. For BTH, Yangtze River Delta, Pearl River Delta and Hubei regions, the reductions of PM2.5 are 24.1, 24.3, 13.5 and 29.5 µg/m3, with the avoided premature deaths of 1066, 1963, 454 and 583, respectively.

Meteo-Hydrological Sensors within the Lake Maggiore Catchment: System Establishment, Functioning and Data Validation.

Climate change and human activities have a strong impact on lakes and their catchments, so to understand ongoing processes it is fundamental to monitor environmental variables with a spatially well-distributed and high frequency network and efficiently share data. An effective sharing and interoperability of environmental information between technician and end-user fosters an in-depth knowledge of the territory and its critical environmental issues. In this paper, we present the approaches and the results obtained during the PITAGORA project (Interoperable Technological Platform for Acquisition, Management and Organization of Environmental data, related to the lake basin). PITAGORA was aimed at developing both instruments and data management, including pre-processing and quality control of raw data to ensure that data are findable, accessible, interoperable, and reusable (FAIR principles). The main results show that the developed instrumentation is low-cost, easily implementable and reliable, and can be applied to the measurement of diverse environmental parameters such as meteorological, hydrological, physico-chemical, and geological. The flexibility of the solutions proposed make our system adaptable to different monitoring purposes, research, management, and civil protection. The real time access to environmental information can improve management of a territory and ecosystems, safety of the population, and sustainable socio-economic development.

Short-Term Impacts of Meteorology, Air Pollution, and Internet Search Data on Viral Diarrhea Infection among Children in Jilin Province, China.

The influence of natural environmental factors and social factors on children's viral diarrhea remains inconclusive. This study aimed to evaluate the short-term effects of temperature, precipitation, air quality, and social attention on children's viral diarrhea in temperate regions of China by using the distribution lag nonlinear model (DLNM). We found that low temperature affected the increase in children's viral diarrhea infection for about 1 week, while high temperature and heavy precipitation affected the increase in children's viral diarrhea infection risk for at least 3 weeks. As the increase of the air pollution index may change the daily life of the public, the infection of children's viral diarrhea can be restrained within 10 days, but the risk of infection will increase after 2 weeks. The extreme network search may reflect the local outbreak of viral diarrhea, which will significantly improve the infection risk. The above factors can help the departments of epidemic prevention and control create early warnings of high-risk outbreaks in time and assist the public to deal with the outbreak of children's viral diarrhea.