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.

Fertilizer, though one of the most essential inputs for increasing agricultural production, is a leading cause of nitrous oxide emissions from agriculture, contributing significantly to global warming. Therefore, understanding factors affecting farmers’ use of fertilizers is crucial to develop strategies to improve its efficient use and to minimize its negative impacts. Using data from 2528 households across the Indo-Gangetic Plains in India, Nepal, and Bangladesh, this study examines the factors affecting farmers’ use of organic and inorganic fertilizers for the two most important cereal crops – rice and wheat. Together, these crops provide the bulk of calories consumed in the region. As nitrogen (N) fertilizer is the major source of global warming and other environmental effects, we also examine the factors contributing to its overuse. We applied multiple regression models to understand the factors influencing the use of inorganic fertilizer, Heckman models to understand the likelihood and intensity of organic fertilizer (manure) use, and a probit model to examine the over-use of N fertilizer. Our results indicate that various socio-economic and geographical factors influence the use of organic and inorganic fertilizers in rice and wheat. Across the study sites, N fertilizer over-use is the highest in Haryana (India) and the lowest in Nepal. Across all locations, farmers reported a decline in manure application, concomitant with a lack of awareness of the principles of appropriate fertilizer management that can limit environmental externalities. Educational programs highlighting measures to improving nutrient-use-efficiency and reducing the negative externalities of N fertilizer over-use are proposed to address these problems.

Nitrous oxide is a potent greenhouse gas, with a global warming potential 298 times greater than that of CO2. In agricultural soils, N2O emissions are influenced by a large number of environmental characteristics and crop management techniques that are not systematically reported in experiments. Random Forest (RF) is a machine learning method that can handle missing data and ranks input variables on the basis of their importance. We aimed to predict N2O emission on the basis of local information, to rank environmental and crop management variables according to their influence on N2O emission, and to compare the performances of RI: with several regression models. RF outperformed the regression models for predictive purposes, and this approach led to the identification of three important input variables: N fertilization, type of crop, and experiment duration. This method could be used in the future for prediction of N2O emissions from local information. (c) 2013 Elsevier Ltd. All rights reserved.

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.