The significant reduction in PM₂.₅ mass concentration after the outbreak of COVID-19 provided a unique opportunity further to study the formation mechanism of secondary inorganic aerosols. Hourly data of chemical components in PM₂.₅, gaseous pollutants, and meteorological data were obtained from January 1 to 23, 2020 (pre-lockdown) and January 24 to February 17, 2020 (COVID-lockdown) in Zhengzhou, China. Sulfate, nitrate, and ammonium were the main components of PM₂.₅ during both the pre-lockdown and COVID-lockdown periods. Compared with the pre-lockdown period, even though the concentration and proportion of nitrate decreased, nitrate was the dominant component in PM₂.₅ during the COVID-lockdown period. Moreover, nitrate production was enhanced by the elevated O₃ concentration, which was favorable for the homogeneous and hydrolysis nitrate formation despite the drastic decrease of NO₂. The proportion of sulfate during the COVID-lockdown period was higher than that before. Aqueous-phase reactions of H₂O₂ and transition metal (TMI) catalyzed oxidations were the major pathways for sulfate formation. During the COVID-lockdown period, TMI-catalyzed oxidation became the dominant pathway for aqueous-phase sulfate formation because the elevated acidity favored the dissolution of TMI. Therefore, the enhanced TMI-catalyzed oxidation affected by the elevated particle acidity dominated the sulfate formation, resulting in the slight increase of sulfate concentration during the COVID-lockdown period in Zhengzhou.

It is enlightening to determine the discrepancies and potential reasons for the degree of impact from the COVID-19 control measures on air quality as well as the associated health and economic impacts. Analysis of air quality, socio-economic factors, and meteorological data from 447 cities in 46 countries indicated that the COVID-19 control measures had significant impacts on the PM₂.₅ (particulate matter with an aerodynamic diameter less than 2.5 μm) concentrations in 20 (reduced PM₂.₅ concentrations of −7.4–29.1 μg m⁻³) of the selected 46 countries. In these 20 countries, the robustly distinguished changes in the PM₂.₅ concentrations caused by the control measures differed between the developed (95% confidence interval (CI): −2.7–5.5 μg m⁻³) and developing countries (95% CI: 8.3–23.2 μg m⁻³). As a result, the COVID-19 lockdown reduced death and hospital admissions change from the decreased PM₂.₅ concentrations by 7909 and 82,025 cases in the 12 developing countries, and by 78 and 1214 cases in the eight developed countries. The COVID-19 lockdown reduced the economic cost from the PM₂.₅ related health burden by 54.0 million dollars in the 12 developing countries and by 8.3 million dollars in the eight developed countries. The disparity was related to the different chemical compositions of PM₂.₅. In particular, the concentrations of primary PM₂.₅ (e.g., BC) in cities of developing countries were 3–45 times higher than those in developed countries, so the mass concentration of PM₂.₅ was more sensitive to the reduced local emissions in developing countries during the COVID-19 control period. The mass fractions of secondary PM₂.₅ in developed countries were generally higher than those in developing countries. As a result, these countries were more sensitive to the secondary atmospheric processing that may have been enhanced due to reduced local emissions.

Intra-urban pollution monitoring requires fine particulate (PM₂.₅) concentration mapping at ultrahigh-resolution (dozens to hundreds of meters). However, current PM₂.₅ concentration estimation, which is mainly based on aerosol optical depth (AOD) and meteorological data, usually had a low spatial resolution (kilometers) and severe spatial missing problem, cannot be applied to intra-urban pollution monitoring. To solve these problems, top-of-atmosphere reflectance (TOAR), which contains both the information about land and atmosphere and has high resolution and large spatial coverage, may be efficiently used for PM₂.₅ estimation. This study aims to systematically evaluate the feasibility of retrieving ultrahigh-resolution PM₂.₅ concentration at a large scale (national level) from TOAR. Firstly, we make a detailed discussion about several important but unsolved theoretic problems on TOAR-based PM₂.₅ retrieval, including the band selection, scale effect, cloud impact, and mapping quality evaluation. Secondly, four types and eight retrieval models are compared in terms of quantitative accuracy, mapping quality, model generalization, and model efficiency, with the pros and cons of each type summarized. Deep neural network (DNN) model shows the highest retrieval accuracy, and linear models were the best in efficiency and generalization. As a compromise, ensemble learning shows the best overall performance. Thirdly, using the highly accurate DNN model (cross-validated R² equals 0.93) and through combining Landsat 8 and Sentinel 2 images, a 90 m and ∼4-day resolution PM₂.₅ product was generated. The retrieved maps were used for analyzing the fine-scale interannual pollution change inner the city and the pollution variations during novel coronavirus disease 2019 (COVID-19). Results of this study proves that ultrahigh resolution can bring new findings of intra-urban pollution change, which cannot be observed at previous coarse resolution. Lastly, some suggestions for future ultrahigh-resolution PM₂.₅ mapping research were given.

The evolution of the bottom water in Amvrakikos Gulf in Ionian Sea at western Greece for a 50-year timespan was assessed by benthic foraminifera assemblages. The degradation of the bottom water of Amvrakikos has been a catalyst for the surface water degradation. The east basin has shown permanent low environmental quality in bottom waters since 1980, while the west basin has been under seasonal hypoxic regime since 2000. The most adverse environmental conditions occurred in 1990–2000 and 2005–2010 coinciding with the recorded fish mortality events. The major cause of the environmental quality improvement of the bottom water is the intrusion of seawater. In western areas of the gulf, where the influence of the seawater is high, the decreased temperature improves the environmental conditions, while in the areas influenced by river discharges (east and northern), the environmental conditions are depended on multiple causes like organic matter input and surface salinity.

The tropical cyclones impact both the eastern and western coasts of India, causing severe socio-environmental problems. This study analyzed shoreline changes and vegetation degradation caused by cyclone Nisarga and monsoon events in Maharashtra coastal zone and Mumbai region, India. In this study, the shoreline change was studied using the Net Shoreline Movement (NSM) statistical technique embedded in the digital shoreline analysis system (DSAS) tool. The effects of the cyclone on the vegetation were mapped using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and the rainfall distribution from Global Precipitation Measurement (GPM) data. The correlation between rainfall data and vegetation loss was analyzed using geographically weighted regression. The results also show that 90% of the events were concentrated in the 80–300 mm classes, being classified as sudden increases. This cyclone caused erosion in 56.32% of the shoreline; the highest erosion level was observed along the coastal zone of Maharashtra (near Mumbai city). Cyclone Nisarga has also impacted the vegetation loss most prominently in the region, with mean EVI in pre-cyclone equal to 0.4 and post-cyclone equal to 0.2. These eco-physical studies using geospatial technology are needed to understand the behavior of changes in shoreline and vegetation and can also help coastal managers plan for resilient coastal systems after the passage of tropical cyclones.

Current studies on air pollutant exposure during pregnancy and orofacial clefts (OFCs) have inconsistent results, and few studies have investigated refined susceptible windows for OFCs. We aim to estimate association between air pollution and OFCs during the first trimester of pregnancy and identify specific susceptible windows. Birth data was obtained from Birth Defects Surveillance Network in Lanzhou from 2014 to 2019. Air pollution data and temperature data were obtained from ambient air monitoring stations and China Meteorological Data Network, respectively. A distribution lag nonlinear model (DLNM) was applied to estimate weekly-exposure-lag-response association between air pollutant levels and OFCs. The study included 320,787 perinatal infants from 2014 to 2019, of which 685 (2.14‰) were OFCs. The results demonstrated that exposure of pregnant women to aerodynamic diameter ≤ 10 μm (PM₁₀) at lag 4–5 weeks was significantly associated with the risk of OFCs, with the greatest impact at the lag 4 week (RR = 1.029, 95% CI = 1.001–1.057). Exposure to sulfur dioxide (SO₂) at lag 2–4 weeks was significantly associated with the risk of OFCs, with the greatest impact at the lag 3 week (RR = 1.096, 95% CI = 1.041–1.177). This study provides further evidence that exposure to air pollution increases the risk of OFCs in the first trimester of pregnancy.

Despite the apparent improvement in air quality in recent years through a series of effective measures, the concentration of PM₂.₅ and O₃ in Chengdu city remains high. And both the two pollutants can cause serious damage to human health and property; consequently, it is imperative to accurately forecast hourly concentration of PM₂.₅ and O₃ in advance. In this study, an air quality forecasting method based on random forest (RF) method and improved ant colony algorithm coupled with back-propagation neural network (IACA-BPNN) are proposed. RF method was used to screen out highly correlated input variables, and the improved ant colony algorithm (IACA) was adopted to combine with BPNN to improve the convergence performance. Two datasets based on two different kinds of monitoring stations along with meteorological data were applied to verify the performance of this proposed model and compared with another five plain models. The results showed that the RF-IACA-BPNN model has the minimum statistical error of the mean absolute error, root mean square error, and mean absolute percentage error, and the values of R² consistently outperform other models. Thus, it is concluded that the proposed model is suitable for air quality prediction. It was also detected that the performance of the models for the forecasting of the hourly concentrations of PM₂.₅ were more acceptable at suburban station than downtown station, while the case is just the opposite for O₃, on account of the low variability dataset at suburban station.

The outbreak of new infectious diseases is threatening human survival. Transmission of such diseases is determined by several factors, with climate being a very important factor. This study was conducted to assess the correlation between the occurrence of infectious diseases and climatic factors using data from the Sentinel Surveillance System and meteorological data from Gwangju, Jeollanam-do, Republic of Korea. The climate of Gwangju from June to September is humid, with this city having the highest average temperature, whereas that from December to February is cold and dry. Infection rates of Salmonella (temperature: r = 0.710**; relative humidity: r = 0.669**), E. coli (r = 0.617**; r = 0.626**), rotavirus (r = − 0.408**; r = − 0.618**), norovirus (r = − 0.463**; r = − 0.316**), influenza virus (r = − 0.726**; r = − 0.672**), coronavirus (r = − 0.684**; r = − 0.408**), and coxsackievirus (r = 0.654**; r = 0.548**) have been shown to have a high correlation with seasonal changes, specifically in these meteorological factors. Pathogens showing distinct seasonality in the occurrence of infection were observed, and there was a high correlation with the climate characteristics of Gwangju. In particular, viral diseases show strong seasonality, and further research on this matter is needed. Due to the current COVID-19 pandemic, quarantine and prevention have become important to block the spread of infectious diseases. For this purpose, studies that predict infectivity through various types of data related to infection are important.

Malaria is one of the most widespread communicable diseases in the southeast regions of Iran, particularly the Chabahar County. Although the outbreak of this disease is a climate-related phenomenon, a comprehensive analysis of the malaria-climate relationship has not yet been investigated in Iran. The aims of this study are as follows: a) analyzing the seasonal characteristics of the various species of the infection; b) differentiating between number of patients during El Niño and La Niña and also during the wet and dry years. The monthly malaria statistics collected from twelve health centers were firstly averaged into seasonal scale and then composited with the corresponding data of the ground-based meteorological records, Southern Oscillation Index (SOI), and the satellite-based rainfall data. The proper statistical tests were used to detect differences in the number of patients between El Niño and La Niña and also between the adopted wet and dry episodes. Infection rate from the highest to the lowest was associated with summer, autumn, spring, and winter, respectively. Plasmodium falciparum, P. vivax, and the other species were responsible for 22%, 75%, and 3% of the sickness, respectively. The outbreak of P. falciparum/P. vivax occurs during autumn/summer. Due to the malaria eradication programs in urban areas, infection statistics collected from the rural areas were found to be more climate-related than that of urban regions. For rural/urban areas, the infection statistics exhibited a significant decline/increase during El Niño episodes. In autumn, spring, and winter, the patient number has significantly increased/decreased during the dry/wet years, respectively. These relationships were, however, reversed in summer.

Architects, hydrologists, agriculturists, and solar engineers require the data of solar radiation for solar technologies such as solar drying, cooking, heating, and building illuminations. The aim of this study is to evaluate the effect of climate change on the potential of solar energy in the Eastern Anatolia Region (EAR) of Turkey. The global warming problem caused by greenhouse gases is increasing due to the increase in the use of fossil origin fuels in our world, and climate change is coming out. In this content, the values of monthly sunshine duration and global solar radiation are provided by the Meteorological Service in Turkish for the EAR of Turkey in the periods between 1987 and 2010. Thus, in a period of 24 years, it is investigated how much change took place in meteorological data. These changes are examined in the two different periods. In conclusion, it is observed that important changes occur in some meteorological data. The highest decrease in the variation amount of yearly average global solar radiation is seen in Erzurum, while the highest increase is seen in Erzincan. The highest increase in the variation amount of yearly average sunshine duration is seen in Erzincan, while the highest decrease is seen in Bitlis. In addition, the statistical analysis (t-test) is made to determine whether the difference between two periods is statistically significant for a considered region.