Contamination Assessment of Heavy Metals in Agricultural Soil, in the Liwa Area (UAE)

Assessment of heavy metals and human health risk associated with the consumption of crops cultivated in industrial areas of Maputo, Mozambique

This study aimed to evaluate heavy metals concentrations in soils and vegetables (cabbage, lettuce, and cassava) cultivated at Matola and Beluluane Industrial Parks, and to assess health risks linked to their consumption through estimated daily intake, hazard index (HI), and incremental lifetime cancer risk. Concentrations of Al, As, Co, Cd, Cr, Ni, Pb, and Zn were determined in the two sites. Soil concentrations of As at Beluluane site and As, Cd, and Cr at Matola site exceeded reference limits of the Food and Agriculture Organization/World Health Organization, showing heavy metal contamination. At Beluluane site, all studied vegetables presented As and Pb levels higher than reference limits, Cd concentrations were higher than the reference limit in cabbage, lettuce, and cassava leaves. At Matola site crops concentrations of As, Cd, Cr, and Pb exceeded the reference limits. Zinc exceeded the reference limit in all crops except in cabbage. HIs for vegetables from Beluluane exceeded 1.0 in cabbage (2.66), lettuce (2.27), and cassava leaves (2.37). Likewise, at Matola, HIs exceeded 1.0 in lettuce (1.67), cassava leaves (1.65), and root tubers (13). We found that vegetables cultivated in industrial parks present high carcinogenic risk due to heavy metal contamination, rendering them unsuitable for human consumption.

Distribution, Source, and Ecological Risk of Heavy Metals in Sewage Irrigation of Taiyuan, Shanxi Province, China

The pollution of agricultural soil by heavy metals is a significant environmental issue that has a serious impact on human health and food security. This study focused on investigating the presence of heavy metal pollution in sewage-irrigated soils in Taiyuan city. A total of 110 soil samples were analyzed for the presence of As, Hg, Cd, Pb, Cr, Cu and Zn. The results showed that the concentrations of these metals ranged from 0.06 to 26.74 mg/kg for As, 0.00 to 0.84 mg/kg for Hg, 0.03 to 0.69 mg/kg for Cd, 44.32 to 100.09 mg/kg for Pb, 9.85 to 42.19 mg/kg for Cr, 13.38 to 53.72 mg/kg for Cu, and 42.77 to 145.47 mg/kg for Zn. The average concentrations of these metals were found to be below the risk values specified in the "Soil environmental quality (GB15618-2018)", except for As and Cd in three sampling points in Xiaodian District. The heavy metal pollution in these areas can be attributed to various sources, such as industrial activities, the use of fertilizers and pesticides, and the irrigation process. According to the geo-accumulation index, the agricultural soil in the Taiyuan irrigation area was found to be uncontaminated by Zn, Cr, Cu, and As, and lightly contaminated by Cd, Hg, and Pb. The Nemerow Pollution Index indicated that the soil at all sampling points exhibited a slight level of pollution. Moreover, the ecological risk assessment indicated that all heavy metals posed a slight level of pollution. The findings of this study provide a scientific basis for the development of effective policies and measures for soil environmental protection and pollution control.

Ecological Risk and Pollution Assessment of Heavy Metals in Farmland Soil Profile with Consideration of Atmosphere Deposition in Central China

Heavy metals (HMs) in agricultural land have caused serious environmental problems, resulting in severe contamination of the food chain and posing potential health threats. This study aims to investigate the pollution levels and potential ecological risks of HMs in farmland soils in central China, taking into account atmospheric deposition. Several indices were used to assess the status of HMs and compare surface soil with deeper soil. Descriptive statistics, Pearson correlation, and UMAP clustering methods were utilized to identify the characteristics of HMs. Additionally, stepwise linear regression models were employed to quantify the contributions of different variables to the potential ecological risks of HMs. The results showed that the average content of Zn in surface soil (289.41 ± 87.72 mg/kg) was higher than in the deeper soil (207.62 ± 37.81 mg/kg), and similar differences were observed in the mean values of related Igeo (1.622 ± 0.453 in surface soil and 1.183 ± 0.259 in deeper soil) and PEI (0.965 ± 0.292 in surface soil and 0.692 ± 0.126 in deeper soil) indices. This indicates that surface soil is more heavily polluted. The UMAP results confirmed the high variability of HMs in the surface soil, while PCA results suggested the importance of pollution and ecological risk indices. The stepwise linear model revealed that different variable structures contribute differently to the risk. In conclusion, Cr and Zn were found to be the major contaminants in the local farmland soil, with higher concentrations in the surface soil. The geoaccumulation and total potential ecological risk were classified as low risk. High variability of HMs was observed in the surface soil. Therefore, HM-related pollution indices and ecological risk indices are important for assessing the contamination status of local HMs. The local potential ecological risk can be attributed to specific heavy metals, each of which can have different effects on the local ecological risk.

Ecological and health risk assessment of heavy metals in agricultural soils from northern China

Soil pollution by heavy metals can cause continuing damage to ecosystems and the human body. In this study, we collected nine fresh topsoil samples and 18 maize samples (including nine leaf samples and nine corn samples) from agricultural soils in the Baiyin mining areas. The results showed that the order of heavy metal concentrations (mg/kg) in agricultural soils was as follows: Zn (377.40) > Pb (125.06) > Cu (75.06) > Ni (28.29) > Cd (5.46) > Hg (0.37). Cd, Cu, Zn, and Pb exceeded the Chinese risk limit for agricultural soil pollution. The average the pollution load index (4.39) was greater than 3, indicating a heavy contamination level. The element that contributed the most to contamination and high ecological risk in soil was Cd. Principal component analysis (PCA) and Pearson's correlation analysis indicated that the sources of Ni, Cd, Cu, and Zn in the soil were primarily mixed, involving both industrial and agricultural activities, whereas the sources of Hg and Pb included both industrial and transportation activities. Adults and children are not likely to experience non-carcinogenic impacts from the soil in this region. Nonetheless, it was important to be aware of the elevated cancer risk presented by Cd, Pb, and especially Ni. The exceedance rates of Cd and Pb in corn were 66.67% and 33.3%, respectively. The results of this research provide data to improve soil protection, human health monitoring, and crop management in the Baiyin district.

Metals stress on soil enzyme activities and herbs defense in the vicinity of high traffic roadways

This study has investigated the impact of vehicle sourced heavy metals (HMs) on soil enzyme activities and plants in and around high traffic roadways near the metropolitan area. In detail, the defense response against HM pollution was studied by considering the commonly available herbs around the roadside area namely Alternanthera paronychioides, Ageratum conyzoides, Spilanthes acmella, and Parthenium hysterophorus. The study reported that the HM concentrations such as Cu, Ni, Zn, Mn, and Cr were observed in the range of 6.05 ± 0.1 to 309 ± 0.5 mg/kg in roadside soil and 5.2 ± 0.1to 451 ± 4.2 mg/kg in the herbs collected from roadside area. The soil enzyme (urease, dehydrogenase, amylase, catalase, peroxidase, and polyphenol oxidase) activities decreased by 22.56 to 77.84% in roadside soil and lower IC50 values were observed for DPPH (2.32-4.67) and H2O2 (1.59-2.15) free radical scavenging activities in plants collected from roadside area. The flavonoid and phenolic content in plants collected from the roadside area ranges from 12.65 ± 0.2 to 15.75 ± 0.3 mg quercitin/g and 0.61 ± 0.04 to 1.16 ± 0.1 mg gallic acid/g respectively while in plant collected from the control areas ranges from 7.96 ± 0.1 to 11.24 ± 0.05 and 0.47 ± 0.01 to 0.61 ± 0.1. In addition, the contamination factor (CF) (1.53-11.92) and geo-accumulation index (Igeo) (0.031-2.99) in soil and bioaccumulation factor (BAF) (0.72-2.73) of Cu, Ni, Zn, Mn, and Crin plants indicated that the soil and plants growing along the highway were heavily contaminated with HM. Finally, Pearson correlation matrix confirmed the inhibition effect of HM on soil enzymatic activities and enzymatic defense of plants in response to the HM stress.

Determination of Soil Quality Index of agricultural land adjacent to industrial sites of Jamuria C.D.Block, Paschim Bardhaman District, West Bengal

A healthy soil is crucial for food security, but human activities, particularly industrialization, are degrading the soil's quality. This study aims to assess and compare the Soil Quality Index (SQI) at three industrial sites: the iron and steel industry, the open cast coal mining industry, and the brick kiln industry, along with a control field. To measure the SQI, the weighted additive method was applied to the nine selected physico-chemical properties of soil: soil temperature, soil moisture, Bulk Density, pH, N, P, K, OC, and EC. Principal Component Analysis coupled with multiple correlation methods was used to determine the Minimum Data Set. The most dominant factors near the iron and steel industrial site are soil temperature, N, K, and EC, while N, OC, soil moisture, P, K, and EC are the most significant factors near the open cast coal mine. In the case of the brick kiln industrial site, soil moisture, OC, K, soil temperature, and P have the highest loadings. The calculated SQI indicates that the quality of soil is in better condition in the control field (0.6475), while the soil adjacent to the coal mining industrial site (0.1426) is in the worst state, followed by the iron and steel industrial site (0.1611) and the brick kiln industrial site (0.289). To attain sustainable agricultural practices in industrial landscapes, efficient management of nutrient contents and phytoremediation can be helpful.

A simple method for microwave-assisted preparation of tire samples

Heavy metals content in tires affects the safety of soil and agricultural products. The digestion method is a pretreatment for determining heavy metals in tire samples, and will affect the efficiency and accuracy of the heavy metal determination. The microwave digestion process and reagents for tire samples are not currently standardized. Therefore, this study attempts to provide an appropriate method of resolution for scholars. All digestion processes were performed in Mars One. We tested 15 different acid mixtures to determine the best reagent type and dose and then investigated the effect of maximum temperature, holding time, and sample grams on the degree of digestion. In summary, the best condition to digest the tire sample was a mixture of 3 ml HNO3 and 7 ml H2SO4, taking 0.1 (± 0.0005) g tire sample, at the maximum digestion temperature of 220 °C for 25 min. The experimental conclusion will provide a reliable experimental method for scientists using MARS One to study heavy metals in tires. At the same time, researchers using the MARS series can also find valuable references in this paper.

Mapping of trace elements in topsoil of arid areas and assessment of ecological and human health risks in Qatar

Soil is the incubator of human activities. Mapping of soil contaminants needs to be constantly updated. It is fragile in arid regions, especially if it accompanies dramatic and successive industrial and urban activities in addition to the climate change. Contaminants affecting soil are changing due to natural and anthropogenic influences. Sources, transport and impacts of trace elements including toxic heavy metals need continuous investigations. We sampled soil in accessible sites in the State of Qatar. An inductively coupled plasma-optical emission spectrometry (ICP-OES) and an inductively coupled plasma-mass spectrometry (ICP-MS) were used to determine the concentrations of Ag, Al, As, Ba, C, Ca, Ce, Cd, Co, Cr, Cu, Dy, Er, Eu, Fe, Gd, Ho, K, La, Lu, Mg, Mn, Mo, Na, Nd, Ni, Pb, Pr, S, Se, Sm, Sr, Tb, Tm, U, V, Yb and Zn. The study also presents new maps for the spatial distribution of these elements using the World Geodetic System 1984 (projected on UTM Zone 39N) which is based on socio-economic development and land use planning. The study assessed the ecological risks and human health risks of these elements in soil. The calculations showed no ecological risks associated with the tested elements in soil. However, the contamination factor (CF) for Sr (CF > 6) in two sampling locations calls for further investigations. More important, human health risks were not detected for population living in Qatar and the results were within the acceptable range of the international standards (hazard quotient HQ < 1 and Cancer risk between 10^-5 and 10^-6). Soil remains a critical component with water and food nexus. In Qatar and arid regions, fresh water is absent and soil is very poor. Our findings enhance the establishment of scientific strategies for investigating soil pollution and potential risks to achieve food security.

Content of Cadmium and Nickel in Soils and Assimilatory Organs of Park Woody Species Exposed to Polluted Air

The rising level of pollutant emissions is becoming one of the most pressing environmental problems of our time. Therefore, this work is focused on evaluating Cd and Ni contamination of soils and assimilatory organs of two native (Acer platanoides L., Taxus baccata L.) and two non-native (Negundo aceroides Moench, Thuja occidentalis L.) woody species in urban parks of SW Slovakia. The contents of Cd and Ni in soils were determined by the AAS method and, in the assimilatory organs of trees, by the AAS-ETA method. The studied soils (Fluvisol, Phaeozem) have neutral soil reactions and a moderate organic matter content. Cadmium soil contamination is considerable to very high; in the case of Ni, it is moderate to low. Cadmium levels detected in leaves were 31% higher than in needles, while Ni levels were 27% lower. Significant ecological factors in relation to the studied woody species were evaluated using PCA. The first three principal components of PCA significantly correlated with Cd (PC1) and Ni (PC3) contents in soils and Cd content in assimilatory organs (PC2), thus suggesting that these elements could especially originate from industrial and vehicular sources. Knowledge of the factors affecting the accumulation of risk elements in the assimilatory organs of park woody species can be successfully used, especially in the assessment of the quality of the urban environment and the selection of suitable cultivars for planting in areas with air pollution.

Assessing Atmospheric Pollution and Its Impact on the Human Health

In recent decades, atmospheric pollution has become a major risk for public health and ecosystems [...]

The Impact of Air Pollution on Pulmonary Diseases: A Case Study from Brasov County, Romania

Air pollution is considered one of the most significant risk factors for human health. To ensure air quality and prevent and reduce the harmful impact on human health, it is necessary to identify and measure the main air pollutants (sulfur and nitrogen oxides, PM10 and PM2.5 particles, lead, benzene, carbon monoxide, etc.), their maximum values, as well as the impact they have on mortality/morbidity rates caused by respiratory diseases. This paper aims to assess the influence of air pollution on respiratory diseases based on an analysis of principal pollutants and mortality/morbidity data sets. In this respect, four types of data are used: pollution sources inventory, air quality data sets, mortality/morbidity data at the local and national level, and clinical data of patients diagnosed with different forms of lung malignancies. The results showed an increased number of deaths caused by respiratory diseases for the studied period, correlated with the decreased air quality due to industrial and commercial activities, households, transportation, and energy production.

Challenges and Opportunities in the Application of Chemometrics in the Pharmaceutical and Food Science Industries

Collapse

On the Spatio-Temporal Characteristics of Aerosol Optical Depth in the Arabian Gulf Zone

The article investigates some of the available measurements (Terra MODIS satellite data) of the aerosol optical depth (AOD) taken in the Arabian Gulf, a zone traditionally affected by intense sand-related (or even sand-driven) meteorological events. The Principal Component Analysis (PCA) reveals the main subspace of the data. Clustering of the series was performed after selecting the optimal number of groups using 30 different methods, such as the silhouette, gap, Duda, Dunn, Hartigan, Hubert, etc. The AOD regional and temporal tendency detection was completed utilizing an original algorithm based on the dominant cluster found at the previous stage, resulting in the regional time series (RTS) and temporal time series (TTS). It was shown that the spatially-indexed time series (SITS) agglomerates along with the first PC. In contrast, six PCs are responsible for 60.5% of the variance in the case of the temporally-indexed time series (TITS). Both RTS and TTS are stationary in trend and fit the studied data series set well.

Impact of Soil Pollution on Melliferous Plants

This study aims at providing bee products and derivatives of medicinal plant consumers with a multifaceted perspective on mineral elements occurring in the soils of two forest zones in the vicinity of North Dobrogea (Romania) by (1) analyzing the pollution levels of the soils at three sites (denoted by DS, PH, and ST) in the study region, using different indicators; (2) providing the results of the transfer of metals from the soil to Sambucus nigra L. (SnL), Hypericum perforatum (Hp), and Tilia tomentosa (Tt). The statistical analysis of the series collected at these locations shows no difference between the elements' concentrations (as a whole). Still, the values of the geo-accumulation index (Igeo) classify the soils as being soils that are moderately to highly contaminated with Cd (and not contaminated with Cu, Mn, or Zn) with respect to the European background values. The cumulative indices-the degree of contamination (DC), the pollution load index (PLI), the Nemerow integrated pollution index (NIPI), and the potential ecological risk index (PERI) indicated the highest contamination in DS (which is a tourist area). To assess the accumulation of different metals in plants, the enrichment factors (EF) were computed. In over 75% of cases, EF was above 1, indicating a high degree of enrichment with different metals. The highest values were those for Cu (41.10 in DS for SnL), and Cd (12.85 in DS for Tt). The results showed that there were different degrees of accumulation between microelements and trace elements in the plants. Tt acted as a bioaccumulator for almost all of the studied elements (K, Mg, Na, Fe, Mn, Cu, Zn, and Cd).

Influence of Anomalies on the Models for Nitrogen Oxides and Ozone Series

Nowadays, observing, recording, and modeling the dynamics of atmospheric pollutants represent actual study areas given the effects of pollution on the population and ecosystems. The existence of aberrant values may influence reports on air quality when they are based on average values over a period. This may also influence the quality of models, which are further used in forecasting. Therefore, correct data collection and analysis is necessary before modeling. This study aimed to detect aberrant values in a nitrogen oxide concentration series recorded in the interval 1 January–8 June 2016 in Timisoara, Romania, and retrieved from the official reports of the National Network for Monitoring the Air Quality, Romania. Four methods were utilized, including the interquartile range (IQR), isolation forest, local outlier factor (LOF) methods, and the generalized extreme studentized deviate (GESD) test. Autoregressive integrated moving average (ARIMA), Generalized Regression Neural Networks (GRNN), and hybrid ARIMA-GRNN models were built for the series before and after the removal of aberrant values. The results show that the first approach provided a good model (from a statistical viewpoint) for the series after the anomalies removal. The best model was obtained by the hybrid ARIMA-GRNN. For example, for the raw NO2 series, the ARIMA model was not statistically validated, whereas, for the series without outliers, the ARIMA(1,1,1) was validated. The GRNN model for the raw series was able to learn the data well: R2 = 76.135%, the correlation between the actual and predicted values (rap) was 0.8778, the mean standard errors (MSE) = 0.177, the mean absolute error MAE = 0.2839, and the mean absolute percentage error MAPE = 9.9786. Still, on the test set, the results were worse: MSE = 1.5101, MAE = 0.8175, rap = 0.4482. For the series without outliers, the model was able to learn the data in the training set better than for the raw series (R2 = 0.996), whereas, on the test set, the results were not very good (R2 = 0.473). The performances of the hybrid ARIMA–GRNN on the initial series were not satisfactory on the test (the pattern of the computed values was almost linear) but were very good on the series without outliers (the correlation between the predicted values on the test set was very close to 1). The same was true for the models built for O3.

Geospatial Assessment of Groundwater Quality with the Distinctive Portrayal of Heavy Metals in the United Arab Emirates

Groundwater is a valuable resource, and its quality is critical to human survival. Optimal farming and urbanization degraded groundwater reserves. This research investigates and reports the spatial variability of selected heavy metals developed in the Liwa area of the United Arab Emirates. Forty water samples were collected from existing wells and analyzed for different elements. Principal components analysis was applied to a subgroup of the data set in terms of their usefulness for determining the variability of groundwater quality variables. Geographic information systems were used to produce contour maps to analyze the distribution of heavy metals. Ordinary kriging was used with Circular, Spherical, Tetraspherical, Pentaspherical-Bessel, K-Bessel, Hole effect, and Stable models for better representation. The water quality index was constructed using heavy metal concentrations and other variables. This yielded a value of 900 beyond the limit stated by WHO and US EPA. Nugget analysis showed that Cd (0), K (7.38%), and SO4 (1.81%) variables exhibited strong spatial dependence. Al (27%), Ba (40.87%), Cr (63%), Cu (34%), EC (27%), HCO3 (56%), NO3(36%), Pb (64%), and TDS (53%) represented moderate spatial dependence. As (76%), Mn (79%), Ni (100%), pH (100%), Temp (93%), and Zn (100%) exhibited weak spatial dependence.

Phytoremediation technology and food security impacts of heavy metal contaminated soils: A review of literature

Heavy metal accumulation in soil and water is one of major problems caused by inorganic contaminants. Their presence in agricultural soils in high quantities have impacted the food security significantly and, by extension, the human health. Amongst various physico-chemical methods available for remediation of heavy-metals-polluted-sites, phytoremediation approaches have been found to be safe and environment friendly. This review gathered scattered information on heavy metal phytoremediation studies published in both review and research articles. It described the impact of heavy metals on food security and comprehensively discussed the application of different phytoremediation approaches for treatment of heavy metal-polluted soils, the basic principles underlining them, their strengths and weaknesses. Our findings indicated that, while hundreds of hyper-accumulator plants are being reported yearly, only few describe limitations inherent in them, such as low growth rate, low biomass production, and low metal tolerance. Hence, this review also gave a detailed overview of research gaps in phytotechnology and advocates consideration of the 'omics' studies; genomics, proteomics, metabolomics and likes in selecting and enhancing potential plants for phytoremediation. For a sustainable large-scale phytoremediation application, we established a multi-technology repair strategy via the combination of different methods like application of biological composts, plant-growth promoting microorganisms, and phytohormones for stimulation of the plant-growth during phytoremediation. We also gave comprehensive insights to proper disposal of plants used for phytoremediation, this subject is often not well considered/planned while deciding the application of plants for removal of heavy metals from polluted environments.

Heavy Metal Assessments of Soil Samples from a High Natural Background Radiation Area, Indonesia

Mamuju, Indonesia, is an area with high natural background radiation. This study assesses heavy metal content in soil samples from this area to determine the level of public and environmental hazard it presents. This study analyzes natural radionuclide elements using high purity germanium (HPGe) gamma spectrometry and performs heavy metals analysis using a flame atomic absorption spectrometry (FAAS). Moreover, pollution indices and descriptive analyses were used to assess heavy metal contamination in the environment and the correlation between heavy metals and radionuclides. The results demonstrate that soil samples in several areas of Mamuju contain a high concentration of the natural radionuclides ²²⁶Ra and ²³²Th, and that heavy metal concentrations in the soil decrease in the sequence Zn > Pb > Cr > Cu > Ni > Cd. This study revealed that soil samples from Mamuju are moderately contaminated. There was a strong positive relationship between ²²⁶Ra, ²³²Th, ambient dose equivalent rate, and Pb. Ecological risk index (RI) and cumulative pollution index (IPI) values in Mamuju are 2.05 and 125, respectively, which are possible hazards to human health as a result. Pb concentration in the Mamuju soil samples ranged from 109 to 744 mg kg⁻¹, exceeding the worldwide average of 27 mg kg⁻¹.

Assessing the Water Pollution of the Brahmaputra River Using Water Quality Indexes

Water quality is continuously affected by anthropogenic and environmental conditions. A significant issue of the Indian rivers is the massive water pollution, leading to the spreading of different diseases due to its daily use. Therefore, this study investigates three aspects. The first one is testing the hypothesis of the existence of a monotonic trend of the series of eight water parameters of the Brahmaputra River recorded for 17 years at ten hydrological stations. When this hypothesis was rejected, a loess trend was fitted. The second aspect is to assess the water quality using three indicators (WQI)-CCME WQI, British Colombia, and a weighted index. The third aspect is to group the years and the stations in clusters used to determine the regional (spatial) and temporal trend of the WQI series, utilizing a new algorithm. A statistical analysis does not reject the hypothesis of a monotonic trend presence for the spatially distributed data but not for the temporal ones. Hierarchical clustering based on the computed WQIs detected two clusters for the spatially distributed data and two for the temporal-distributed data. The procedure proposed for determining the WQI temporal and regional evolution provided good results in terms of mean absolute error, root mean squared error (RMSE), and mean absolute percentage error (MAPE).

Study of Atmospheric Pollution and Health Risk Assessment: A Case Study for the Sharjah and Ajman Emirates (UAE)

Dust is a significant pollution source in the United Arab Emirates (UAE) that impacts population health. Therefore, the present study aims to determine the concentration of heavy metals (Cd, Pb, Cr, Cu, Ni, and Zn) in the air in the Sharjah and Ajman emirates’ urban areas and assesses the health risk. Three indicators were used for this purpose: the average daily dose (ADD), the hazard quotient (HQ), and the health index (HI). Data were collected during the period April–August 2020. Moreover, the observation sites were clustered based on the pollutants’ concentration, given that the greater the heavy metal concentration is, the greater is the risk for the population health. The most abundant heavy metal found in the atmosphere was Zn, with a mean concentration of 160.30 mg/kg, the concentrations of other metals being in the following order: Ni > Cr > Cu > Pb > Cd. The mean concentrations of Cd, Pb, and Cr were within the range of background values, while those of Cu, Ni, and Zn were higher than the background values, indicating anthropogenic pollution. For adults, the mean ADD values of heavy metals decreased from Zn to Cd (Zn > Ni > Cr > Cu > Pb > Cd). The HQ (HI) suggested an acceptable (negligible) level of non-carcinogenic harmful health risk to residents’ health. The sites were grouped in three clusters, one of them containing a single location, where the highest concentrations of heavy metals were found.

Determination of Heavy Metal Contamination and Pollution Indices of Roadside Dust in Dhaka City, Bangladesh

Urban roadside dust samples from Dhaka City in Bangladesh were collected from a planned residential area (PRA), spontaneous residential area (SRA), commercial area (CA), and urban green area (UGA) in winter and summer to study how season and different urban land-use categories influence the concentrations of heavy metals (Cr, Mn, Co, Ni, Cu, Zn, As, and Pb) and different pollution indices. The dust samples were fractionated into <32 μm particles, extracted by acid digestion followed by estimation of heavy metals, using ICP-MS. Pollution indices were calculated from the metal concentrations, using standard protocols. The concentrations of heavy metals in roadside dust varied significantly (all p < 0.05), due to sampling seasons and the land-use category. Higher concentrations of heavy metals (Cr, Mn, Ni, Cu, Zn, and Pb) were found in the dust sampled during the winter season than in the summer season, except for As and Co. The geo-accumulation index (Igeo) indicated that the commercial area was heavily contaminated with Cu and Zn during the winter season. The contamination factor (CF) was higher for Cu and Zn in the CA, PRA, and SRA of Dhaka City in winter than in the summer season. The enrichment factor (EF) suggested that Mn and Co were the least enriched metals, and significant enrichment was seen for Cu and Zn for all land-use categories, both in summer and winter. A moderate potential ecological risk for Cu was estimated in CA and PRA in the winter season.

Estimating the Precipitation Amount at Regional Scale Using a New Tool, Climate Analyzer

Different methods are known for interpolating spatial data. Introduced a few years ago, the initial version of the Most Probable Precipitation Method (MPPM) proved to be a valuable competitor against the Thiessen Polygons Method, Inverse Distance Weighting and kriging for estimating the regional trend of precipitation series. Climate Analyzer, introduced here, is a user-friendly toolkit written in Matlab, which implements the initial and modified version of MPPM and new selection criteria of the series that participate in estimating the regional precipitation series. The software provides the graphical output of the estimated regional series, the modeling errors and the comparisons of the results for different segmentations of the time interval used in modeling. This article contains the description of Climate Analyzer, accompanied by a case study to exemplify its capabilities.

Assessment of Metals Concentrations in Soils of Abu Dhabi Emirate Using Pollution Indices and Multivariate Statistics

The aim of this study was twofold. Firstly, we performed a land capability class determination of the agricultural soils from the Abu Dhabi Emirate, the United Arab Emirates, based on the concentrations of 17 chemical elements determined in the soil samples collected from 84 locations. Secondly, we assess the soil pollution with different metals, using several pollution indices. The results of Principal Component Analysis (PCA) shows that four principal components (PCs) are responsible for describing the total metals concentrations’ variance, the highest contribution on PC1 being that of Mn, and Cr, on PC2 that of Fe, on PC3 that of Cu, and on PC4 that of Al. After determining the optimal number of clusters, we classified the sites into three clusters, while the studied metals were grouped function on their concentrations. Then, we used five indices to assess the pollution level of the soil at the study sites and in the clusters. The geo—accumulation index (I_geo) indicates uncontamination/moderately contamination with Cu in cluster 1, uncontaminated/moderately contaminate soils with Cd, Cu, and Ni in cluster 2, and uncontaminated/moderately contaminated soil with Cu and moderately contaminated with Pb, Zn, and Ni in cluster 3. By comparison, the enrichment factors overestimate the pollution of the studied sites. The pollution load index (PLI) indicates a baseline level of pollution at 14 sites and the deterioration of the soil quality at four sites. The Nemerow pollution index provides similar results as PLI.