Monthly Dataset Research Articles

ON THE RELATIONSHIP BETWEEN ECONOMIC POLICY UNCERTAINTY, GEOPOLITICAL RISK AND STOCK MARKET RETURNS IN SOUTH KOREA: A QUANTILE CAUSALITY ANALYSIS

In this research, we assess the influence of geopolitical risk (GPR), exchange rate (EXCH) and economic policy uncertainty (EPU) on South Korea stock market. Using monthly dataset covering the period from 1997 to 2021, we utilized the novel non-parametric causality-in-quantiles test initiated by Balcilar et al. (2017) to assess these associations. This study discovered that the effect of macroeconomic shocks in South Korea is diverse across the stock market, implying that the influence of these shocks is not consistent across the stock market in South Korea. Furthermore, this study revealed that the causal influence of EPU, GPR on the stock market is visible in mean and variance. Nonetheless, the causal influence of EXCH on the stock market can only be seen in the mean, with no indication of causation in the variance. We consider that these results are significant to South Korean stock market investors for the purposes of foreign portfolio diversification and establishing investing strategies during times of economic uncertainty.

At odds? How European governments decided on public health restrictions during COVID-19

ObjectivesThis study aimed to understand how politics, economics, and public health restrictions affected each other during the COVID-19 pandemic. MethodsWe use seemingly unrelated regressions on a monthly data set of government approval ratings, the stringency index, the time-dependent reproduction number (R), and unemployment, allowing the residuals in each regression to be correlated with each other. We also conduct sensitivity tests using weekly data and the growth in polls. ResultsThe study covers 27 European countries from April 2020 to April 2021. A unit increase in the R and COVID-19 cases per million increases the stringency index by 23.742 and 4.207, respectively; a unit increase in stringency boosts the incumbent’s popularity by 0.384; the poll positively affects the stringency index; stringency has negative effects on the R; and the poll and stringency index have opposite effects on unemployment. ConclusionPolitical and economic pressures did not hinder the government from introducing stronger measures.

Natural resources commodity prices volatility and economic uncertainty: Evaluating the role of oil and gas rents in COVID-19

Indeed, natural resources are the most tradable goods across the globe and are responsible for running the industrial sector of each country. However, with the recent outbreak of Covid-19, the demand and supply chain of natural resources is greatly influence and causes economic recession, which needs to be addressed. The Covid-19 pandemic increases uncertainty in the global economic policy and natural resource commodity prices. Although the literature provides empirical outcomes in the pre-Covid-19 period, still the influence in the Covid-19 remained unexplored and needs further research. In this regard, the current study examined the US natural resources commodity price volatility and global economic uncertainty while utilizing the monthly dataset from November 01, 2007, to May 01, 2021. Using the wavelet power spectrum and wavelet coherence approaches, the empirical findings of the wavelet power spectrum reveal volatilities in the global economic policy uncertainty and crude oil price in the short run. In contrast, the oil rent, natural gas rents and total natural resources show long-run vulnerabilities. Additionally, the wavelet coherence unveils bidirectional causal association global economic policy uncertainty and other variables. However, the causal influence of global economic performance is found higher than the others. Besides, the crude oil prices and oil rents are associated with global economic policy uncertainty after the Covid-19 pandemic. These findings could have essential economic and energy implications for governors, policymakers, and researchers.

The Internet search engines as an additional tool in public health research in the context of disease outbreaks

PurposeThe study aims to help epidemiologists identify new patterns and trends in spreading infections on the example of the current coronavirus disease 2019 (COVID-19) pandemic using data from search engines. The study identified the types of thematic search of Russian Internet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19. The study aims to determine digital epidemiology search trends to the current COVID-19 pandemic. The study identified the types of thematic search of RuNet users and queries that have a mathematically confirmed correlation with public health indicators: mortality and morbidity from COVID-19.Design/methodology/approachThe authors explored two types of data: (1) the monthly datasets of keywords relevant to COVID-19 extracted from the Yandex search engine and (2) officially published statistics data. Alongside, the authors searched for associations between all variables in this dataset. The Benjamin–Hochberg correction for multiple hypothesis testing was applied to the obtained results to improve the reliability of the results. The authors built a unique website with opportunities to update datasets and designed dashboards to visualize the research outcomes using PHP and Python.FindingsThe research results show the number of significant relationships that the authors interpreted in epidemiology as a new instrument in Public Health research. There are 132 data combinations with a correlation higher than 75%, making it possible to determine a mathematically reliable relationship between search statistics trends and mortality/morbidity indicators. The most statistically significant effects identified in bundles “query” – “query”, “query” – “morbidity”, “query” – “mortality”.Originality/valueThe authors developed a new approach in analyzing outbreaks of infections and their consequences based on a comprehensive analysis of epidemiological and infodemic data. The research results are relevant to public health as other decision-making and situational analysis tools for citizens and specialists who want to receive additional confirmation for the indicators of the official statistics of the headquarters for control and monitoring of the situation with coronavirus and others infections.

Long-term change in relative humidity across China from 1961-2018

Using observational data of daily relative humidity obtained from 2479 meteorological stations from 1961-2018 and the homogenized monthly data set of national ground meteorological stations, this paper analyzes the climate change characteristics of average relative humidity across China as well as in 9 individual regions. From 1961-2018, the climate change trend in annual average relative humidity across China was negligible, but there were large differences among different regions. Overall, annual relative humidity in the Qinghai-Tibet Plateau and Xinjiang area showed a significant increase, while Northwest China, Northeast China and northern North China showed a significant decrease. Seasonally, average relative humidity in China chiefly decreased in spring, and there was no discernable trend in other seasons. The Qinghai-Tibet Plateau showed a significantly increasing trend in winter, spring and summer, especially in spring; Xinjiang exhibited a significant increasing trend in winter, summer and autumn; Northeast China presented a significantly decreasing trend in winter; Northwest China, North China, Central China and East China witnessed a significantly decreasing trend in spring; and South China and Southwest China showed a slightly decreasing trend across all 4 seasons. Large urban agglomerations exert an influence on relative humidity to some degree, with urban areas experiencing lower relative humidity than rural areas.

ATMOSPHERIC RADIOCARBON FOR THE PERIOD 1950–2019

ABSTRACTThis paper presents a compilation of atmospheric radiocarbon for the period 1950–2019, derived from atmospheric CO2 sampling and tree rings from clean-air sites. Following the approach taken by Hua et al. (2013), our revised and extended compilation consists of zonal, hemispheric and global radiocarbon (14C) data sets, with monthly data sets for 5 zones (Northern Hemisphere zones 1, 2, and 3, and Southern Hemisphere zones 3 and 1–2). Our new compilation includes smooth curves for zonal data sets that are more suitable for dating applications than the previous approach based on simple averaging. Our new radiocarbon dataset is intended to help facilitate the use of atmospheric bomb 14C in carbon cycle studies and to accommodate increasing demand for accurate dating of recent (post-1950) terrestrial samples.

MSG-GAN-SD: A Multi-Scale Gradients GAN for Statistical Downscaling of 2-Meter Temperature over the EURO-CORDEX Domain

One of the most important open challenges in climate science is downscaling. It is a procedure that allows making predictions at local scales, starting from climatic field information available at large scale. Recent advances in deep learning provide new insights and modeling solutions to tackle downscaling-related tasks by automatically learning the coarse-to-fine grained resolution mapping. In particular, deep learning models designed for super-resolution problems in computer vision can be exploited because of the similarity between images and climatic fields maps. For this reason, a new architecture tailored for statistical downscaling (SD), named MSG-GAN-SD, has been developed, allowing interpretability and good stability during training, due to multi-scale gradient information. The proposed architecture, based on a Generative Adversarial Network (GAN), was applied to downscale ERA-Interim 2-m temperature fields, from 83.25 to 13.87 km resolution, covering the EURO-CORDEX domain within the 1979–2018 period. The training process involves seasonal and monthly dataset arrangements, in addition to different training strategies, leading to several models. Furthermore, a model selection framework is introduced in order to mathematically select the best models during the training. The selected models were then tested on the 2015–2018 period using several metrics to identify the best training strategy and dataset arrangement, which finally produced several evaluation maps. This work is the first attempt to use the MSG-GAN architecture for statistical downscaling. The achieved results demonstrate that the models trained on seasonal datasets performed better than those trained on monthly datasets. This study presents an accurate and cost-effective solution that is able to perform downscaling of 2 m temperature climatic maps.

Non-mortgage loans in the Chinese residential real estate market

PurposeFrom January 2021, the potential flow of Chinese household non-mortgage loans, including business loans and short-term consumption loans to the residential real estate market, has attracted the attention of the regulatory authorities. This study aims to examine the effects of household non-mortgage loans on the Chinese residential real estate market.Design/methodology/approachBased on a monthly data set between July 2011 and December 2019, this study adopts a cointegration analysis.FindingsThis study finds that household non-mortgage loans do play a significant role in driving residential real estate prices in China.Originality/valueWhile many studies have examined the Chinese real estate market and its linkage with the financial system and the economy, this study is the first of its kind in the academic literature that exclusively focusses on the role of non-mortgage loans in real estate prices, and makes an original contribution.

SEASONAL EPIDIOMOLOGY OF DIARRHOEA, MALARIA AND PNEUMONIA RELATIVE TO CLIMATIC FACTORS: EVIDENCE FROM ASABA, SOUTH-SOUTHERN, NIGERIA

Understanding the seasonal epidemiology of diarrhoea, malaria and pneumonia relative to climatic factors is the thrust of the study. Empirical monthly data sets used covered the period of 2012M1-2019M12. Graphical exploration, methods of seasonal variability and Chi-square test of homogeneity were employed. The results showed that diarrhoea, malaria and Pneumonia are very high at the peak of rainy season (July-September) especially August, when humidity is relatively high and temperature is low. The result also indicted that the null hypothesis of no seasonal variation in the incidence of malaria and pneumonia is rejected in favour of the alternative as Chi-squared values are significant under 5% level respectively. This implies that the incidence of malaria and pneumonia have seasonal occurrence. However, the findings are imperative for the diseases seasonal determination, surveillance and control in Asaba, south-southern Nigeria. KEYWORDS: Seasonal epidemiology, Diarrhoea, malaria, pneumonia, climatic factors

Evaluation and Projection of Wind Speed in the Arid Region of Northwest China Based on CMIP6

Near surface wind speed has significant impacts on ecological environment change and climate change. Based on the CN05.1 observation data (a gridded monthly dataset with the resolution of 0.25 latitude by 0.25 longitude over China), this study evaluated the ability of 25 Global Climate Models (GCMs) from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the wind speed in the Arid Region of Northwest China (ARNC) during 1971–2014. Then, the temporal and spatial variations in the surface wind speed of ARNC in the 21st century were projected under four Shared Socioeconomic Pathways (SSPs), SSP1-2.6, SSP2-4.5, SSP3-7.0, and SP5-8.5. The results reveal that the preferred-model ensemble (PME) can fairly evaluate the temporal and spatial distribution of surface wind speed with the temporal and spatial correlation coefficients exceeding 0.5 at the significance level of p = 0.05 when compared to the 25 single models and their ensemble mean. After deviation correction, the PME can reproduce the distribution characteristics of high wind speed in the east and low in the west, high in mountainous areas, and low in basins. Unfortunately, no models or model ensemble can accurately reproduce the decreasing magnitude of observed wind speed. In the 21st century, the surface wind speed in the ARNC is projected to increase under SSP1-2.6 scenario but will decrease remarkably under the other three scenarios. Moreover, the higher the emission scenarios, the more significant the surface wind speed decreases. Spatially, the wind speed will increase significantly in the west and southeast of Xinjiang, decrease in the north of Xinjiang and the south of Tarim Basin. What’s more, under the four scenarios, the surface wind speed will decrease in spring, summer and autumn, especially in summer, and increase in winter. The wind speed will decrease significantly in the north of Tianshan Mountains in summer, decrease significantly in the north of Xinjiang and the southern edge of Tarim Basin in spring and autumn, and increase in fluctuation with high values in Tianshan Mountains in winter.

Summer monsoon rainfall variations and its association with atmospheric circulations over Sudan

This work investigates the factors influencing summer (June–September: JJAS) monsoon rainfall variability over Sudan using monthly datasets from multiple sources from 1980 to 2018. Sequential Mann-Kendall, Empirical Orthogonal Function (EOF), composite and correlation analysis were utilized to investigate the spatio-temporal rainfall variability in Sudan, and its associated circulations and teleconnections. Results revealed a significant increase in JJAS rainfall during the study period. The wet condition was found with accompanying convergence winds at low levels and divergence in the upper levels, while the dry conditions were due to the weakening of convergence moisture transport. A hot Sea Surface Temperature anomalies over the Eastern Pacific Ocean (Nino3.4) are associated with dry conditions,whilst cold anomalies are associated with wet conditions. There was correlation between the first EOFs JJAS rainfall and EOFs SSTs teleconnections NINO3.4. IOD, PDO, and NAO, exhibited moderate to strong positive correlations of 62.5% in average. However, the second mode of SSTs teleconnections EOFS revealed a low positive correlation of 46.4% in average, excluding the strong negative impact of NINO3.4–73%. In different regions in Sudan, the influences of NINO3.4 are much stronger in the south, west and central regions (0.71, −0.71 and −0.72). And IOD exhibited much impact in the south and west regions (0.73, 0.74). The southern and western regions had the highest correlation with PDO and NAO. ENSO and IOD also tend to extend summer rainfall season. Therefore, this research serves as the baseline study for further work to examine the impact of SSTs teleconnections on summer rainfall in Sudan (starting, cessation and extremes), thereby providing a reasonable basis for monitoring and predicting similar conditions in the future.

The Role of House Prices in Monetary Transmission Mechanism: Evidence from Turkish Economy

It is possible for central banks to carry out their policies effectively by making the correct assessment of the reflection of their monetary policy decisions on real economic activities. For this reason, it is important to examine the effects of applied policies on real economic activities and inflation. Accordingly, the 2008 global financial crisis revealed the importance of the relationship between monetary policy, house prices, and real economic activity, in other words, the channel of transmission of house prices. As a matter of fact, developments in the housing industry have a significant impact on the process leading up to the 2008 Global financial crisis. In this context, after the 2008 Global financial crisis, both the role of house prices in monetary policy implementation and the function of house prices in the transmission mechanism has begun to be questioned. In this study, the effectiveness of the house prices transmission channel, a subgroup of the asset prices channel based on the experience of the 2008 Global financial crisis, was empirically analyzed based on the Turkish economy. The VAR model has been preferred as a method of empirical analysis. The analysis covers the period 2010-2019, and the monthly data set is used in the analysis. The results of the analysis show that monetary policy affects house prices, but house prices do not have a statistically significant effect on housing investment, industrial production index, and inflation. Related results show that the housing prices transmission channel is not active in the Turkish economy. Key Words: Monetary Transmission Mechanism, House Prices, VAR Model JEL Classification: E52

A monthly night-time light composite dataset of NOAA-20 in China: a multi-scale comparison with S-NPP

ABSTRACT Night-Time light (NTL) data have been widely used for monitoring the dynamics of human activities and socioeconomics. As a new-generation satellite for acquiring NTL data, the National Oceanic and Atmospheric Administration-20 (NOAA-20) was successfully launched in November 2017. To support broad-scale environmental applications, it is necessary to generate monthly NTL composites of NOAA-20. Taking China as the study area, we produced NOAA-20 monthly NTL composites from April 2019 to December 2019. First, we performed a series of de-noising steps to eliminate NOAA-20 NTL pixels affected by sunlight, moonlight, high scan angles, clouds and further eliminated NTL outliers in time series by using a modified z-score method. Then, we aggregated daily NOAA-20 NTL data to produce monthly NTL composites to improve data coverage and stability. Subsequently, we examined the consistency of monthly composites between NOAA-20 and S-NPP at multiple scales, including provincial, prefectural, and pixel levels. Our results show that the monthly NTL composites of NOAA-20 are in good agreement with that of S-NPP with an R2 ranging from 0.81 to 0.99. Besides, we found that the spatial variation trends of the NOAA-20 monthly NTL composites are similar to that of S-NPP monthly NTL composites. We believe that the monthly NTL composites of NOAA-20 are very close to that of S-NPP and will open up more opportunities for relevant NTL studies.

Catalog of Long-term Transient Sources in the First 10 yr of Fermi-LAT Data

Abstract We present the first Fermi Large Area Telescope (LAT) catalog of long-term γ-ray transient sources (1FLT). This comprises sources that were detected on monthly time intervals during the first decade of Fermi-LAT operations. The monthly timescale allows us to identify transient and variable sources that were not yet reported in other Fermi-LAT catalogs. The monthly data sets were analyzed using a wavelet-based source detection algorithm that provided the candidate new transient sources. The search was limited to the extragalactic regions of the sky to avoid the dominance of the Galactic diffuse emission at low Galactic latitudes. The transient candidates were then analyzed using the standard Fermi-LAT maximum likelihood analysis method. All sources detected with a statistical significance above 4σ in at least one monthly bin were listed in the final catalog. The 1FLT catalog contains 142 transient γ-ray sources that are not included in the 4FGL-DR2 catalog. Many of these sources (102) have been confidently associated with active galactic nuclei (AGNs): 24 are associated with flat-spectrum radio quasars, 1 with a BL Lac object, 70 with blazars of uncertain type, 3 with radio galaxies, 1 with a compact steep-spectrum radio source, 1 with a steep-spectrum radio quasar, and 2 with AGNs of other types. The remaining 40 sources have no candidate counterparts at other wavelengths. The median γ-ray spectral index of the 1FLT-AGN sources is softer than that reported in the latest Fermi-LAT AGN general catalog. This result is consistent with the hypothesis that detection of the softest γ-ray emitters is less efficient when the data are integrated over year-long intervals.

Trends of sulfur and nitrogen deposition from 2003 to 2017 in Japanese remote areas

Emissions of sulfur (S) and nitrogen (N) compounds in East Asia has drastically changed over the last two decades. To assess the influence of the drastic changes in air pollution on ecosystems in Japan, we investigated the trends of S and N deposition during 2003–2017 at remote sites of Acid Deposition Monitoring Network in East Asia (EANET). We measured wet deposition and inferentially estimated dry deposition of S and N using monitoring data from 2003 to 2017 at eight sites. We estimated dry deposition using the inferential method with an updated parameterization for gaseous surface resistance. The linear regression method and nonparametric Mann–Kendall test was used to analyze the temporal trends based on the monthly data sets. High S and N deposition amounts over 10 kg ha−1 year−1 were frequently found at most sites. There were significant increase trends in N deposition to S deposition (N/S) ratio at all sites throughout the 15-year period. Some trends were significantly found when the 15-year period was divided into three: 2003–2007, 2008–2012, and 2013–2017. S deposition had significantly decreased over a wide area in Japan, especially at Sado-seki, Happo, Oki, Hedo, and Ogasawara, in 2013–2017. Significant decreases in oxidized N deposition at Sado-seki and Oki were also found in 2013–2017. Because of almost flat N deposition mainly contributed by reduced N deposition, the N/S ratio clearly increased. These trends were associated with the recent reductions in SO2 and NOx emissions in China. The NOx emission reduction of China has not caught up with that of SO2, and NH3 emissions have not been reduced. This caused the significant increases in the N/S ratio not only in 2013–2017 but also in 2003–2017.

Tsallis q-Stat and the Evidence of Long-Range Interactions in Soil Temperature Dynamics.

The complexities in the variations of soil temperature and thermal diffusion poses a physical problem that requires more understanding. The quest for a better understanding of the complexities of soil temperature variation has prompted the study of the q-statistics in the soil temperature variation with the view of understanding the underlying dynamics of the temperature variation and thermal diffusivity of the soil. In this work, the values of Tsallis stationary state q index known as q-stat were computed from soil temperature measured at different stations in Nigeria. The intrinsic variations of the soil temperature were derived from the soil temperature time series by detrending method to extract the influences of other types of variations from the atmosphere. The detrended soil temperature data sets were further analysed to fit the q-Gaussian model. Our results show that our datasets fit into the Tsallis Gaussian distributions with lower values of q-stat during rainy season and around the wet soil regions of Nigeria and the values of q-stat obtained for monthly data sets were mostly in the range for all stations, with very few values q closer to 1.2 for a few stations in the wet season. The distributions obtained from the detrended soil temperature data were mostly found to belong to the class of asymmetric q-Gaussians. The ability of the soil temperature data sets to fit into q-Gaussians might be due and the non-extensive statistical nature of the system and (or) consequently due to the presence of superstatistics. The possible mechanisms responsible this behaviour was further discussed.

Use of big data for estimation of impacts of meteorological variables on environmental radiation dose on Ulleung Island, Republic of Korea

In this study, the relationship between the environmental radiation dose rate and meteorological variables was investigated with multiple regression analysis and big data of those variables. The environmental radiation dose rate and 36 different meteorological variables were measured on Ulleung Island, Republic of Korea, from 2011 to 2015. Not all meteorological variables were used in the regression analysis because the different meteorological variables significantly affect the environmental radiation dose rate during different periods, and the degree of influence changes with time. By applying the Pearson correlation analysis and stepwise selection methods to the big dataset, the major meteorological variables influencing the environmental radiation dose rate were identified, which were then used as the independent variables for the regression model. Subsequently, multiple regression models for the monthly datasets and dataset of the entire period were developed.

Data mining techniques for predicting the financial performance of Islamic banking in Indonesia

PurposeThe purpose of this paper is to apply various data mining techniques for predicting the financial performance of Islamic banking in Indonesia through the main exogenous determinants of profitability by choosing the best data mining technique based on the criteria of the highest accuracy score of testing and training.Design/methodology/approachThis paper used data mining techniques to predict the financial performance of Islamic banking by applying all of LASSO regression, random forest (RF), artificial neural networks and k-nearest neighbor (KNN) over monthly data sets of all the full-fledged Islamic banks working in Indonesia from January 2011 until March 2020. This study used return on assets as a real measurement of financial performance, whereas the capital adequacy ratio, asset quality and liquidity management were used as exogenous determinants of financial performance.FindingsThe experimental results showed that the optimal task for predicting the financial performance of Islamic banking in Indonesia is the KNN technique, which affords the best-predicting accuracy, and gives the optimal knowledge from the financial performance of Islamic banking determinants in Indonesia. As well, the RF provides closer values to the optimal accuracy of the KNN, which makes it another robust technique in predicting the financial performance of Islamic banking.Research limitations/implicationsThis paper restricted modeling the financial performance of Islamic banking to profitability through the main determinants of return of assets in Indonesia. Future research could consider enlarging the modeling of financial performance using other models such as CAMELS and Z-Score to predict the financial performance of Islamic banking under data mining techniques.Practical implicationsOwing to the lack of using data mining techniques in the Islamic banking sector, this paper would fill the literature gap by providing new effective techniques for predicting financial performance in the Islamic banking sector using data mining approaches, which can be efficient tools in business and management modeling for financial researchers and decision-makers in the Islamic banking sector.Originality/valueAccording to the author’s knowledge, this paper is the first that provides data mining techniques for predicting the financial performance of the Islamic banking sector in Indonesia.

Impact of Commodity Price on Freight Market Considering the 2008 Crisis: An Investigation of Iron Ore Price and Capesize Shipping Rates

This study investigated the asymmetric causality from iron ore price to freight market through Capesize rates by considering the possible impact of the 2008 global economic crisis. The study used a monthly data set of 233 observations covering the period between January 2000 and June 2019. According to the structural break tests, a break was detected in June 2008 and the analyses were performed for two separate periods as pre-crisis and post-crisis in addition to the whole period. The results for the whole period revealed that positive shocks in iron ore price caused negative shocks and negative shocks caused both negative and positive shocks. The divided sample results revealed that positive shocks in the iron ore prices were causes of the positive shocks in the freight rates in the pre-crisis period, while negative shocks in the ore prices were causes of the negative shocks in freight rates, and negative shocks were the causes positive shocks in the post-crisis period. These results suggested that the impact of commodity prices on the freight market before and after the crisis might vary.

Assessment of industrial wastewater for potentially toxic elements, human health (dermal) risks, and pollution sources: A case study of Gadoon Amazai industrial estate, Swabi, Pakistan

In this study, industrial wastewater and groundwater were comparatively investigated for their physicochemical properties, concentrations of potentially toxic elements (PTEs), human health risks and pollution source(s). Every month, 34 wastewater samples and 26 groundwater samples were collected, for a duration of one year. The results showed that the physicochemical parameters and concentrations of PTEs in the industrial wastewater exceeded the maximum permissible limits of Pakistan Environmental Protection Agency (2000). Specifically, it was found that total dissolved solids (5%), total suspended solids (190%), chemical oxygen demand (107%), five-days biochemical oxygen demand (5.7 times), grease/oil (27.1 times), Fe (67%), Zn (29%), Mn (32%), Cu (27%), Ni (16%), Cr (8%), Pb (106%), and Cd (80%) were higher than the permissible limits. The carcinogenic and non-carcinogenic dermal health risks for wastewater irrigation group were significantly higher than the groundwater irrigation group. The hazard index of irrigation with industrial wastewater was 180 times higher than the groundwater. The principal component analysis indicated that industry was the main polluting source. The cluster analysis results of all PTEs (except Fe) were found in the same clade in the dendrogram, which showed a strong similarity within the monthly data set of the whole year. The study recommends using adjacent groundwater instead of industrial wastewater for irrigation purposes.