This investigation aims to study synoptic analysis in the dynamic structure accompanied by air pollution of extreme heat during July 2019 in the Yazd province. The time-series data analysis for the yearly surface air temperatures during the past two decades shows a significant peak surface air temperature in July 2019 in Yazd province. The long-term mean and anomalies of the daily basis (2001 to 2019) for the daily mean sea level pressure show a decrease in pressure with a maximum of about 6 hPa and an increase in geopotential height at 500 hPa with a maximum of about 20-30 gpm (geopotential meter), which has led to an increase in the average daily temperature of about 2 to 4 degrees Celsius. Also, showed high values for Ozone mass mixing ratio over the study area mostly over the west with a maximum of ~92 ppb in Yazd province on 1 July 2019. The AIRS (Atmospheric Infrared Sounder on NASA's Aqua satellite) data shows a positive trend (2003- 2019) for the total daytime Ozone column-averaged over the study area during July. Furthermore, the results of this work obtained from OMI satellite observation show a significant increase in the ultraviolet aerosol index (UVAI) during the study period time. This study shows the recent extreme weather changes in the study area which may be necessary for a better future forecast for heat warnings along with poor air quality and health risk when such events may happen in the future.

Climate change is a complicated issue with many factors playing role in its formation and distribution. Considering this complication, a comprehensive and holistic approach is needed for a better understanding and management of those factors. The causal frameworks are among systemic and integrated methods for addressing the causes of environmental problems and the relationships that exist between the environmental systems in order to propose proper solutions. The DPSIR model is a functional analysis framework that depicts the cause-effect relationships that exist in creating environmental problems. Tehran is one of the major megacities in the Middle East that faces environmental consequences of overpopulation and unplanned urban sprawl, and being located in an arid region, makes it vulnerable to rise of temperature and reduction of precipitation. In this research, by using the DPSIR framework, different aspects of climate condition of Tehran are analyzed and later with the help of this conceptual framework, strategies for controlling climate change are presented.

Global fire cases in recent years and their vast damages are vivid reasons to study the wildfires more deeply. A 25-year period natural wildfire database and a wide array of environmental variables are used in this study to develop an artificial neural network model with the aim of predicting potential fire spots. This study focuses on non-human reasons of wildfires (natural) to compute global warming effects on wildfires. Among the environmental variables, this study shows the significance of temperature for predicting wildfire cases while other parameters are presented in a next study. The study area of this study includes all natural forest fire cases in United States from 1992 to 2015. The data of eight days including the day fire occurred and 7 previous days are used as input to the model to forecast fire occurrence probability of that day. The climatic inputs are extracted from ECMWF. The inputs of the model are temperature at 2 meter above surface, relative humidity, total pressure, evaporation, volumetric soil water layer, snow melt, Keetch–Byram drought index, total precipitation, wind speed, and NDVI. The results show there is a transient temperature span for each forest type which acts like a threshold to predict fire occurrence. In temperate forests, a 0.1-degree Celsius increase in temperature relative to 7-day average temperature before a fire occurrence results in prediction model output of greater than 0.8 for 4.75% of fire forest cases. In Boreal forests, the model output for temperature increase of less than 1 degree relative to past 7-day average temperature represents no chance of wildfire. But the non-zero fire forest starts at 2 degrees increase of temperature which ends to 2.62% of fire forest cases with model output of larger than 0.8. It is concluded that other variables except temperature are more determinant to predict wildfires in temperate forests rather than in boreal forests.

Though, the attention of researchers on exploring the impact of economic policy uncertainty on carbon emissions is on increase, however, the impact of different types of economic policy uncertainty remains unexplored. Thus, this study investigates the impact of different types of economic policy uncertainty on carbon emissions in Japan. A monthly data from 1987M1 to 2019M12 was used, while the FMOLS, DOLS, CCR and ARDL estimators were employed for examining the cointegration among the variables, as well as the long- and short-run relationship between types of economic policy uncertainty and carbon emissions. The study findings revealed a long-run cointegration among energy consumption, per capita income, fiscal, exchange rate, monetary, and trade policy uncertainties and carbon emissions. Moreover, this study found energy consumption, exchange rate, monetary, and trade policy uncertainties to contribute significantly to the increase of carbon emissions in Japan. Finally, this study suggests that environmental policy makers in Japan should take into account the economic policy uncertainty so as to promote robust information for climate policy that will be targeted at ameliorating the carbon emissions in Japan.

The present study attempts to introduce a method of statistical downscaling with a synoptic view. The precipitation data of Golestan Province has been used for the years 1971 to 2010. Employing multivariable regression, this study models the precipitation gauges in the station scale, by making use of 26 predicting components of model HadCM3, on the basis of two A2 and B2 scenarios. However, the minimum predicting components for precipitation in station scale included 26 components for one grid to 390 atmosphere circulation components for the 15 suggested grids. Nevertheless, results indicate minimum error, related to the precipitation models, based on projecting components of the studies of 15 grids. By applying this selected method, the precipitation gauges for 2020 to 2040 has been simulated. General results of the precipitation changes for the yearly decennial average of Golestan Province indicates additive stream of this component, based on both A2 and B2 scenarios. Yet this yearly decennial addition of precipitation go with seasonal and annual changes, i.e. getting drier in summer as well as its subsequent increase in draught issue on one hand, and increased centralization of precipitations in the winter and lack of its proper distribution during year on the other. As a result, changes in local patterns of precipitations throughout the province is promising for maximum increase of precipitation for the farthest southwest area of Golestan, greatly potential for decreasing precipitation of sub eastern area.

International audience | A dynamic coupled biogeochemical-ecological model was used to simulate the effects of nitrogen deposition and climate change on plant communities at three forest sites in France. The three sites had different forest covers (sessile oak, Norway spruce and silver fir), three nitrogen loads ranging from relatively low to high, different climatic regions and different soil types. Both the availability of vegetation time series and the environmental niches of the understory species allowed to evaluate the model for predicting the composition of the three plant communities. The calibration of the environmental niches was successful, with a model performance consistently reasonably high throughout the three sites. The model simulations of two climatic and two deposition scenarios showed that climate change may entirely compromise the eventual recovery from eutrophication of the simulated plant communities in response to the reductions in nitrogen deposition. The interplay between climate and deposition was strongly governed by site characteristics and histories in the long term, while forest management remained the main driver of change in the short term.

The socio-economic and institutional systems of a developing country like India have a big role in the effects of perception on the choice of adapting capability. The study uses exploratory factor analysis to better understand these implications in a regional context (EFA). Therefore, survey research is carried out in Sindhudurg district of coastal Maharashtra, with 410 respondents, assessing perception. EFA leads to the unpacking of latent constructs evaluating the perception of climate change, which in turn affects adaptive capacity and livelihood resilience. These constructs are biophysical impact cognition, motivation to change, economic diversification, and adaptive skills, which together account for 50% of coastal fishermen’s perception of climate change. Multivariate analysis of variance (MANOVA) revealed differences in the interpretation of these factors among coastal fishermen from various backgrounds (MANOVA). Overall, the research emphasizes the importance of perception in determining adaptive choices and resilience. According to the findings, developing adaptation-friendly infrastructural areas is recommended for society’s resilient functioning.

Ugii Lake is a freshwater lake located in the steppe region of Mongolia and is an important breeding and staging area for a wide variety of waterfowl. Remote sensing and geographic information system techniques were used to estimate fluctuations in the surface area and water balance of Ugii Lake. To estimate the changes in lake water balance, lake water fluctuations should be analyzed using the most accurate methods. A different water extraction technique was applied, and the results were compared with field surveys conducted in May, July, and September 2020. The lake surface area using both NDWI and MNDWI-1 showed a strong, positive correlation (R=0.93, R=0.94, p < 0.01) with the water level of Ugii Lake. A topographic map of Ugii Lake was provided by the project (P2018-3568) conducted in August 2019 and used to estimate the volume of Ugii Lake in ArcGIS 10.1. This result was consistent with that of a previous study by JICA in 2005. Finally, the water balance of Ugii Lake was estimated, and the results proved that the influence of both surface and groundwater on Ugii Lake are valuable parameters, which are completely dependent on hydrological regime changes mostly due to local climate change in steppe regions. This study provides valuable insight into the most suitable water extraction methods for lakes in semi-arid steppe regions in Mongolia.

Mangroves play a vital role in protecting the coastal community from the climate change effect and in the restoration of the coastal ecosystem. This research has been designed to determine the spatial and seasonal changes of potentially toxic elements’ (PTEs) concentration in sediments and their potential source contribution among the different human-driven processes in Sundarbans, Bangladesh. Different pollution evaluation indices, random forest (RF) model, conditional inference tree (CIT), self-organizing map (SOM), geographical information system (GIS), and principal component analysis (PCA) were used for the interpretation of sources and risk assessment of PTEs. The mean concentration of PTEs both in winter and monsoon seasons has fallen below the threshold effect level but exceeded the rare effect level of marine sediments quality standards. Results showed that the PTEs were significantly enriched (EF > 1.00 < 70.00) in sediments, whereas the Cd enrichment (7.00% samples) was very alarming (EF = 60–70). Except for Zn and Cd, other PTEs were enriched in 30–60% samples. The highest geoaccumulation and contamination factors for Cd were observed in 46–72% of samples. The ecological risk (ER) factors showed similar results where Cd showed strong to very strong factors (ER = 110–2218) in 80% of samples. The CIT explained the natural/geogenic and anthropogenic sources of pollution, where the higher CIT values for Cd indicated industrial, aquaculture, and coal-based thermal powerplant. The RF model provided that shrimp firms, power plants, industry, and seaport were recognized as the influential sources for Zn, Pb, Cr, Cd, and As in sediments. Though Pb and As were found as the most significant pollutants, Cd was identified as a severe threat to ecology and public health. Based on CIT, RF, SOM and PCA the order of PTEs in mangroves sediment were:industrial/urban > aquaculture/shrimpfirm > powerplant > seaportoperation > tourism > geogenic/natural. The present study will help the policymakers for effective and sustainable management of the mangrove ecosystem.

Around the world, green tides are happening with increasing frequency because of the dual effects of increasingly intense human activity and climate change; this leads to significant impacts on marine ecology and economies. In the last decade, the world's largest green tide, which is formed by Ulva/Enteromorpha porifera, has become a recurrent phenomenon every year in the southern Yellow Sea (China), and it has been getting worse. To alleviate the impacts of such green tide outbreaks, multiple measures need to be developed. Among these approaches, biotechnology plays important roles in revealing the outbreak mechanism (e.g., molecular identification technology for algal genotypes), controlling and preventing outbreaks at the origin sites (e.g., technology to inhibit propagation), and utilizing valuable algal biomass. This review focuses on the various previously used biotechnological approaches that may be applicable to worldwide seaweed blooms that result from global climate change and environmental degradation.