An epidemiological, strategic and response analysis of the COVID-19 pandemic in South Asia: A population-based observational study

Introduction: South Asia has had a dynamic response to the ongoing COVID-19 pandemic. The overall burden and response have remained comparable across highly-burdened countries within the South Asian Region. Methodology: Using a population-based observational design, all eight South Asian countries were analyzed using a step-wise approach. Data were obtained from government websites and publicly-available repositories for population dynamics and key variables. Results: South Asian countries have a younger average age of their population. Inequitable distribution of resources centered in urban metropolitan cities within South Asia is present. Certain densely populated regions in these countries have better testing and healthcare facilities that correlate with lower COVID-19 incidence per million populations. Trends of urban-rural disparities are unclear given the lack of clear reporting of the gaps within these regions. COVID-19 vaccination lag has become apparent in South Asian countries, with the expected time to complete the campaign being unfeasible as the COVID-19 pandemic progresses. Conclusion: With a redesigning of governance policies on preventing the rise of COVID-19 promptly, the relief on the healthcare system and healthcare workers will allow for adequate time to roll out vaccination campaigns with equitable distribution. Capacity expansion of public health within the Region is required to ensure a robust healthcare response to the ongoing pandemic and future infectious disease outbreak

Evaluating the Performance of Different Artificial Intelligence Techniques for Forecasting: Rainfall and Runoff Prospective

The forecasting plays key role for the water resources planning. Most suitable technique is Artificial intelligence techniques (AITs) for different parameters of weather forecasting and generated runoff. The study compared AITs (RBF-SVM and M5 model tree) to understand the rainfall runoff process in Jhelum River Basin, Pakistan. The rainfall and runoff of Jhelum river used from 1981 to 2012. The Different rainfall and runoff dataset combinations were used to train and test AITs. The data record for the period 1981–2001 used for training and then testing. After training and testing, modeled runoff and observed data was evaluated using R2, NRMSE, COE and MSE. During the training, the dataset C2 and C3 were found to be 0.71 for both datasets using M5 model. Similar results were found for dataset of C3 using RBF-SVM. Over all, C3 and C7 were performed best among all the dataset. The M5 model tree was performed better than other applied techniques. GEP has also exhibited good results to understand rainfall runoff process. The RBF-SVM performed less accurate as compare to other applied techniques. Flow duration curve (FDCs) were used to compare the modeled and observed dataset of Jhelum River basin. For High flow and medium high flows, GEP exhibited well. M5 model tree displayed the better results for medium low and low percentile flows. RBF-SVM exhibited better for low percentile flows. GEP were found the accurate and highly efficient DDM among the AITs applied techniques. This study will help understand the complex rainfall runoff process, which is stochastic process. Weather forecasting play key role in water resources management and planning

Quantitative Estimation of the Impact of Precipitation and Land Surface Change on Hydrological Processes through Statistical Modeling

Precipitation variability and land surface changes are the two primary factors that affect basin hydrology, and thus estimation of their impact is of great importance for sustainable development at a catchment scale. In this study, we investigated the long-term changes in precipitation and runoff, from 1961 to 2011, in the Yihe River basin by Mann-Kendall test. A new method of trend pattern was put forward and used to identify the trends of precipitation and runoff, which indicated that the basin had a decreasing trend in annual runoff. The change point occurred in the year 1985 dividing the long-term series into two periods. Precipitation elasticity and linear regression methods were used to quantify the impact of precipitation and land surface change on runoff and provided consistent results of the percentage change in an annual runoff for the postchange period. Use of these methods reveals that the reduction in annual runoff is mainly due to precipitation variability of 56.38–67.68% and land surface change of 43.62–32.32%, as estimated by precipitation elasticity and linear regression methods, respectively. Due to the rapid growth of urbanization, the land surface change increased from 1990 to 2010. The result of this study can provide a reference for the management of regional water resources

Risks of Glaciers Lakes Outburst Flood along China Pakistan Economic Corridor

The China-Pakistan Economic Corridor (CPEC) passes through the Hunza River basin of Pakistan. The current study investigates the creation and effects of end moraine, supra-glacial, and barrier lakes by field visits and remote sensing techniques along the CPEC in the Hunza River basin. The surging and moraine type glaciers are considered the most dangerous type of glaciers that cause Glacial Lake Outburst Floods (GLOFs) in the study basin. It can be concluded from the 40 years observations of Karakoram glaciers that surge-type and non-surge-type glaciers are not significantly different with respect to mass change. The recurrent surging of Khurdopin Glacier resulted in the creation of Khurdopin Glacial Lake in the Shimshal valley of the Hunza River basin. Such glacial lakes offer main sources of freshwater; however, when their dams are suddenly breached and water drained, catastrophic GLOFs appear and pose a great threat to people and infrastructure in downstream areas. This situation calls for an in-depth study on GLOF risks along the CPEC route and incorporation of GLOF for future policy formulation in the country for the CPEC project so that the government may take serious action for prevention, response to GLOFs, and rehabilitation and reconstruction of the areas

Vulnerability Assessment and Application of Bacterial Technology on Urban Rivers for Pollution Eradication

To protect against the environmental pollution, the present research was undertaken to enumerate the Bacterial Technologies (BTs) on the restoration of polluted urban rivers, that is, Fenghu-Song Yang River (FSR) and Xuxi River (XXR). Experimental research accounted for the physiochemical parameters (pH; temperature; dissolved oxygen (DO); chemical oxygen demand (COD); total phosphorus (TP); total nitrogen (TN); and ammonia nitrogen (NH3N)) before and after the BT operation. The results declared that the BT is efficient to restore the polluted rivers up to reliable condition. These results were analyzed by using multivariate statistical techniques (principal component analysis (PCA) and cluster analysis (CA)). These techniques interpreted the complex data sets and expressed the point source information about the water quality of these rivers at SA5, SA6, and SB3 under highly polluted regions. For better understanding, water quality index (WQI) was applied to compute the single numeric value. WQI results are evidence of the above results which prove the water quality of both rivers faced under outrageous condition (below 50 WQI scores) before the BT treatment, but, after the treatment, the rivers were restored from fair to good level (above 50 WQI scores) and overall output of these scores was quite similar to detect the point source of pollution. These results described an abrupt recovery of the urban rivers up to reliable condition for aquatic organism and clear effluents from the rivers

Integrated Evaluation of Urban Water Bodies for Pollution Abatement Based on Fuzzy Multicriteria Decision Approach

Today’s ecology is erected with miscellaneous framework. However, numerous sources deteriorate it, such as urban rivers that directly cause the environmental pollution. For chemical pollution abatement from urban water bodies, many techniques were introduced to rehabilitate the water quality of these water bodies. In this research, Bacterial Technology (BT) was applied to urban rivers escalating the necessity to control the water pollution in different places (Xuxi River (XXU); Gankeng River (GKS); Xia Zhang River (XZY); Fenghu and Song Yang Rivers (FSR); Jiu Haogang River (JHH)) in China. For data analysis, the physiochemical parameters such as temperature, chemical oxygen demand (COD), dissolved oxygen (DO), total phosphorus (TP), and ammonia nitrogen (NH3N) were determined before and after the treatment. Multicriteria Decision Making (MCDM) method was used for relative significance of different water quality on each station, based on fuzzy analytical hierarchy process (FAHP). The overall results revealed that the pollution is exceeding at “JHH” due to the limit of “COD” as critical water quality parameter and after treatment, an abrupt recovery of the rivers compared with the average improved efficiency of nutrients was 79%, 74%, 68%, and 70% of COD, DO, TP, and NH3N, respectively. The color of the river’s water changed to its original form and aquatic living organism appeared with clear effluents from them

Application of Microbial Technology Used in Bioremediation of Urban Polluted River: A Case Study of Chengnan River, China

Contrary to the constraints in time, investment, and management of the traditional technology for waste water treatment, this paper seeks to propose a more advanced, reliable, and affordable new technology to restore urban polluted rivers to pristine quality levels. The paper also presents new ideas on the selection and use of microbial agents to improve the efficiency of pollution removal. It presents the successful implementation of microbial technology (MT) on Chengnan River, which was heavily polluted before MT implementation. Without artificial aeration, sediment dredging, or complete sewage interception, we directly sprayed a previously configured HP-RPe-3 Microbial Agent into the water body and sediment. We considered the feasibility of MT for treating polluted urban rivers from the perspective of several water quality indices evaluation methods. After the treatment, the concentration of dissolved oxygen (DO) reached 5.0 mg/L, the removal rates of ammonia nitrogen (NH3-N) and chemical oxygen demand (COD) reached 20% and 38% respectively, and the average degradation rate of total phosphorus (TP) along river was close to 15%. Also, the Nemerow Index of the river was reduced from 2.7 to 1.9. The Fuzzy Comprehensive Index shows a tendency for improvement from Inferior Grade V to a better grade (approximately Grade III). The color of the river water changed, from black or dark green, to its original color. The results indicate that the bioremediation technology of directly adding microbial agents mainly aimed for the degradation of NH3-N can preliminarily eliminate the black-odor phenomenon of urban rivers, and improve their water quality. It is expected that the MT application, and the concept of how to select the corresponding microbial agents according to main pollutants, can be widely accepted and applied to similar cases

An Automatic Determining Food Security Status: Machine Learning based Analysis of Household Survey Data

Household food security is a major issue in developing countries like Pakistan. Despite significant breakthroughs in grain production within the country, the problem of food availability and utilization persists. Diet is one of the most potent determinants of nutritional condition. The dietary intake method has been utilized to determine the food security status of households, which depends on various factors. There are no automatic and user-friendly methods available to decide food security status, which is generally determined by manually calculating calorie intakes. Due to its high performance and precision, machine learning holds major significance. In this paper, the status of food security has been examined by applying machine learning algorithms, namely, support vector machine, naïve Bayes, k-nearest neighbors, random forest, logistic regression, and neural network, on survey data of households for best predicting the status. A food analysis (FA) app has been developed to automatically predict the FAO status of a household’s food security by implementing the random forest model that found higher precision among algorithms. Additionally, the proposed mobile app will also be helpful for collecting the households’ data. Furthermore, the objective of the study was to enhance food security awareness among individuals

Temperature mediated influence of mycotoxigenic fungi on the life cycle attributes of Callosobruchus maculatus F. in stored chickpea

Environmental factors (biotic and abiotic) are major depletion reasons in granaries. Fungi and insect pests act synergistically in deterioration of grains in storages which results in nutritional damage to the stored food which becomes unpalatable for the consumer. There is a need to establish a timeline for synergistic damage caused by insect pests and mycotoxigenic fungi for better management. For this purpose, interaction of mycotoxigenic fungi (Aspergillus flavus, Aspergillus niger, Penicillium digitatum and Alternaria alternata) with Callosobruchus maculatus (F.) (Coleoptera: Chrysomelidae) was studied at different temperatures. Development of C. maculatus was observed on fungus inoculated and uninoculated C. arietinum seeds. In fungus inoculated grains the development (Fecundity, larval emergence, pupation rate and adult emergence) of C. maculatus was found more better as compared on uninoculated grain. The population of C. maculatus was decreased by increase in temperature but high temperatures favours more fungi developments. More egg laying was observed at 27 °C and 33 °C. At tested temperatures, larval emergence was high as compared to other observed life attributes. Infestation of A. flavus and A. niger was also increased with increase of temperatures. Penicillium digitatum and A. alternata infestation were increased in the C. arietinum at 27 °C and 30 °C respectively. This study will help in measuring the control practices of fungi and insect pest infestations in stored C. arietinum (chickpea) in Pakistan