The aim of this study is to determine the amount of quantitative and qualitative changes in groundwater in the Sarvestan plain in south of Fars province, which is one of the critical plains in Iran in terms of water resources. In this research, zoning maps of electrical conductivity of water in GIS were prepared and various hydrochemical diagrams were illustrated. Different quality parameters of water resources were compared according to the statistical data collected and the experiments performed at the beginning of the 8-year period of the research. Chemical analysis of water samples shows that the groundwater type of most of the studied wells at the beginning of the period (2013) has changed from Ca-Cl and Mg-Cl types to Na-Cl type at end of the time period (2020). Determining the trend of chemical changes shows that the diversity of water samples in terms of anions and cations in water with increasing salinity at the end of the period is less than the variety of samples at the beginning of the period. According to the results of chemical experiments, evaporation, crystallization, and weathering of rocks are the factors that control the composition of groundwater in the study area. This study shows increasing the salinity of groundwater in addition to decreasing precipitation and high water use for agricultural application, due to the type of geological formations, especially the presence of salt domes at groundwater inlets to the plain on the east side of the study area.

Atmospheric models today represent all significant aerosol components. Atmospheric aerosols play an important role in the air, globally through their action on the Earth's radiation balance and locally through their effects on health in heavily polluted areas, they vary considerably in their properties that affect the way they absorb and disperse radiation, and they can thus have a cooling or warming effect, they impact on the formation and life of clouds is one example. Among the main sectors of activity releasing emissions of PM10 (fine particles with a diameter of less than 10 µm) and a diameter of less than 2.5 µm (PM2.5) is the industrial sector, in particular the extraction industry of building materials. The aerosols emitted by this type of industry are composed mainly of a mixture of dust, sulphates, carbon black and nitrates, is clearly perceptible in many continental regions of the northern hemisphere. Improvements in in situ, satellite and surface measurements are needed. However, the mechanisms by which aerosols interact with the environment are extremely complex and still poorly understood. This study is based on satellite atmospheric models to have spatiotemporal variability of concentrations of fine particles smaller than 10 µm in diameter (PM10) and smaller than 2.5 µm in diameter (PM2.5) at the level of the western Rif part of Morocco, home to a large number of extraction quarries and thus offering a high-resolution particle capture system (PM10 and PM2.5).

Mining activities are generally known to enhance the concentration of primordial radionuclides in the environment thereby contributing immensely to human exposure to ionizing radiation of terrestrial origin. Thus, the abandoned Tin and Cassiterite mining site in Oyun, Kwara State, Nigeria, is believed to cause radiological implications on local residents.  Assessment of radon concentration in surface water from the study area was carried out using RAD7-Active Electronic detector big bottle system. In order to ascertain the risk or hazard incurable in consuming such water, 12 samples were analysed and used in the estimation of annual effective dose of radon. The measured maximum and minimum radon concentrations were found to be 44.95 and 21.03 Bq/L with average of 35.86 Bq/L. These values are quite greater than the United Nations Scientific Committee on the Effects of Atomic Radiation (UNSCEAR) recommended limits of 11.1.Bq/L. The estimated total effective dose (AEDEtotal) was found to be within the range of 206.52 and 441.41 μSvy-1, and an average of 352.20 μSvy-1 for Adults, 283.30 and 605.47 μSvy-1, and average of 483.10 μSvy-1 for Children, and finally, 321.70 and 687.47 μSvy-1 with average of 548.64 μSvy-1 for Infants, respectively. These values were higher than the recommended limit of 100 µSvy-1 and 200 µSvy-1 for adult and children respectively. Furthermore, worries should be noted about the probabilistic cumulative effect on the consumers of such water if the ingestion is for an extended period of time.

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.

In this work, we study  the short-term dynamics of the Surface  Air Temperature (SAT) using data obtained  from a  meteorological station in Bogotá from 2009 to 2019  and using  time series.  The data that we used correspond to the  monthly mean of the historical registers of SAT and three  pollutants. A descriptive analysis of  the data follows. Then, some predictions are obtained from two different approaches:  (i) a univariate analysis of SAT through a  SARIMA model, which shows a good fit; and  (ii) a multivariate analysis of SAT and  pollutants  using a SVAR model. Suitable transformations were first applied on the original dataset to work with stationary time series. Subsequently, A SARIMA model and a VAR(2) with its associated SVAR model are estimated. Furthermore, we obtain one-year forecasts for the logarithm of SAT in both models. Our forecasts simulate the natural fluctuation of SAT, presenting peaks and valleys in months when SAT is high and low, respectively. The SVAR model allows us to identify certain shocks that affect the instant relationships among variables. These relations were studied by the impulse-response function and the VAR model variance decomposition. Although the statistical methods used in this study are classical, they continue being widely used in the environmental field, presenting god fits, and the results obtained in this study are consistent with  environmental theories.

A monthly temporal monitoring of the physico-chemical parameters of the leachate from the Greater Casablanca “Mediouna” open-air landfill in Morocco over a period of 13 months was carried out to show their variability over time. This monitoring also highlights the effect of rainfall on leachate quality through fluctuations observed in wet and dry periods. Indeed, the leachate was sampled from a collector that drains a mixture of young and mature leachate. Several physico-chemical parameters were studied: pH, temperature, conductivity, organic matter (BOD5 and COD), total matter (TS, TVS), nitrogen (N-NO2-, N-NO3-, N-NH4+, TKN), total phosphorus (Tp), salts (Cl-, SO42-) and metals (Cd, Co, Cr, Ca, Cu, Fe, K, Mg, Mn, Ni, Pb, Zn). As a result, significant concentrations were recorded throughout the monitoring for the majority of the parameters, showing a high aggressiveness of the leachate. Also, statistically significant relationships were observed between the different parameters. On the other hand, the leachate pollution index (LPI) was calculated to determine the overall potential of leachate pollution. The identification and study of the behaviour of the physico-chemical parameters is very useful for the design of an adequate leachate treatment plant for the Greater Casablanca landfill "Mediouna", taking into consideration the extreme values recorded during the monitoring period, in order to avoid any malfunctioning due to an underestimation of the pollution.

Sustainable urbanization under sustainable development goals requires quantitative information on urban landscape. Despite having the fastest growth of urban area and poor air quality, Faridabad, a constituent district of National Capital Region, fails to gain much research attention.  Present study based on multi-temporal; freely available satellite image has indicated 3% increase in the built-up against 2% decrease arable land from 2008 to 2018. Further, spatial metrics (Shanon’s entropy, class area (CA), number of patches (NP), largest patch index (LPI)) has indicated scattered development of built-up. Increase CA (11470 ha in 2008 and 13806 ha in 2018) and NP (221 in 2008 and 476 in 2018) have indicated isolated development of built-up with small area coverage. Increase in LPI (12.5% in 2008 and 13.5% in 2018) of built up indicated compact growth of dense built-up in the southern and eastern side leading to the vertical expansion of the city area. Linear expansion of the residential built-up, industrial, and commercial area along the highways, roads and railways and vehicular emission has contributed to the high aerosol concentration. While, in the rural region the high aerosol loading has also been observed because of the extensive use of fertilizer and stubble burning. Present research on land-use land cover changes and its impact on air quality could be contributed significantly in urban policy making for climate change adaptation and mitigation strategies.

The study was conducted to assess surface water quality in Ca Mau peninsula using multivariate statistical analysis. Fifty-one water samples with the parameters of pH, dissolved oxygen (DO), total suspended solids (TSS), biochemical oxygen demand (BOD5), chemical oxygen demand (COD), ammonium (N-NH4+), orthophosphate (P-PO43-) and total coliform were used in the evaluation. Water quality is assessed using national standard and water quality index (WQI). The methods of cluster (CA), discriminant (DA), principal component analysis (PCA) were used to analyze the variation patterns of water quality. The surface water was contaminated with organic matters, suspended solids, nutrients, and microorganisms. DA revealed that DO, TSS, BOD5 and pH contributed 76.91% to the seasonal variation of water quality. Water quality is classified from bad to heavily polluted. CA grouped water quality into 7 clusters and DO, TSS, BOD5, COD and coliform of the clusters 1-3 were significantly higher than those of the clusters 4-7. PCA presented that PC1-PC3 was the main sources affecting water quality, explaining 85.45% of the variation in water quality. The sources of pollution can be human (domestic wastewater, waste from agriculture, fisheries, industry, landfills), natural (hydrological regime, rainwater overflow, river bank erosion). pH, DO, BOD5, COD, TSS, N-NH4+, P-PO43- and coliform have an impact on water quality and need to be continuously monitored. However, for the multivariate statistical method to be more effective, an initial data set with several water quality parameters sampling locations is needed. The current results provide scientific information and support local water quality monitoring activities.

This study carries the evaluation of tolerance of sixteen plants against pollution. These plants have been selected and assessed for several phyto-socio-economic (tree height, canopy, type of tree, laminar structure, hardiness and economic value) and biochemical qualities (pH, Relative water content (R), Ascorbic acid (AA), Chlorophyll a (Chl a), (Chl b), Carotenoids (Car) and total Chlorophyll (TC)) and tested for Air pollution tolerance index (APTI) and Expected performance Index (EPI) and then EPI score used as grades of plants (Not Recommended, Very poor, Poor, Moderate, Good, Very good, Excellent and Best Plus Plant). Statistical analysis tool like correlation matrix among plant parameters and ANNOA test has been applied to understand the relationship among plant parameters and plant species. The best EPI score means best suited plant for the area for plantation and green belt development while plant with lower EPI may be used as bioindicators for the pollution because they are very sensitive for the air pollution.

Exposure to household dust is a common occurrence in all countries and causes various diseases. This study provided information on the number, shape, size distribution, and elemental composition of household dust particles collected in urban homes in Agadir city in Morocco. Moreover, a potential human health risk of exposure has been identified based on current research. Samples were analyzed using computer-controlled scanning electron microscopy and ImageJ image processing program. A total of 3296 particles were analyzed for their size, and 76 particles were classified according to their size and elemental composition. Household dust particles were classified in six types: micro-aggregates (31.6%), biogenic (5.3%), spherical (17.1%), subrounded (7.9%), subangular (11.8%), and angular (26.3%). These particles were determined to have originated from a distant source (Trask classification index between 1 and 2.5). They were large (Skewness asymmetry coefficient > 1), and ranged from 0.2 to 363 µm with an average value of 22.8 ± 0.6 µm in diameter. Dust particles with diameters of 5-10 µm and 10-20 µm were the most abundant, while dust diameters of 10-20 µm, 20-30 µm, and > 100 µm were the highest in volume. The domestic dust deposition rate was 19.8 ± 7.4 g/m2 per year. Household dust is one of the major sources of PM10 in the residential environment (44.6% of the total number of particles), and the studied properties of house dust are highly related to human health. Household dust is a critical element to be considered in the occurrence of respiratory and cardiovascular infections.