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As the GA-FIS algorithm predicts BOD with fewer errors than PSO-FIS and the aim of this study is to provide an accurate prediction of missing data, GA-FIS was only used to predict the BOD parameter; the other parameters were predicted by PSO-FIS algorithm. As a result, the model successfully could provide outstanding performance for predicting the BOD, COD, NH3-N, OG, pH and SS with MAE equal to 3.79, 5.14, 0.4, 0.27, 0.02, and 3.16, respectively. In September 2014, the Kashmir valley (north-west India) experienced a massive flood causing significant economic losses and fatalities. This disaster underlined the high vulnerability of the local population and raised questions regarding the resilience of Kashmiris to future floods. Although the magnitude of the 2014 flood has been considered unprecedented within the context of existing measurements, we argue that the short flow series may lead to spurious misinterpretation of the probability of such extreme events. Here we use a millennium-long record of past floods in Kashmir based on historical and tree-ring records to assess the probability of 2014-like flood events in the region. Our flood chronology (635 CE-nowadays) provides key insights into the recurrence of flood disasters and propels understanding of flood variability in this region over the last millennium, showing enhanced activity during the Little Ice Age. We find that high-impact floods have frequently disrupted the Kashmir valley in the past. Thus, the inclusion of historical records reveals large flood hazard levels in the region. The newly gained information also underlines the critical need to take immediate action in the region, so as to reduce the exposure of local populations and to increase their resilience, despite existing constraints in watershed management related to the Indus Water Treaty. Achieving a circular economic model in agriculture and meeting the food requirement of the growing population is a global challenge. The task is much more daunting in the Eastern Himalaya where low productive maize-fallow is a predominant production system. To enhance system productivity and energy use efficiency while maintaining environmental sustainability and economic profitability, therefore, energy-efficient, low carbon footprint (CF; CO2-e) and profitable short duration crops must be made an integral part of the maize fallow system. Thus, six cropping systems viz., maize-fallow, maize-French bean, maize-soybean, maize-black gram, maize-green gram, and maize-toria were evaluated for seven consecutive years (2011-2018) to assess their energy requirement and efficiency, carbon footprint (CF; CO2-e), economic returns and eco-efficiency. The results revealed that the maize-French bean system had the highest system productivity (11.4 Mg ha-1), energy productivity (17.9), energy profitability (15.9) and non-renewable energy use efficiency (9.97). The maize-French bean system had also the highest net profit (US$ 3764.5 ha-1) and benefit to cost ratio (2.54). The energy consumed under different inputs/activities across the cropping systems for chemical fertilizers, diesel and machinery ranged from 50.0-62.7%, 17.3-20.8% and 4.6-15.4%, respectively. The maize-fallow system had the highest CF (0.34 kg CO2 e per kg grain) while, the maize-French bean system had the lowest CF (0.19 kg CO2 e per kg grain). The maize-French bean system had also considerably increased eco-efficiency both in terms of energy use (US$ 0.23 MJ-1) and (US$ 1.78 per kg CO2 e) over maize-fallow system. Thus, the study has suggested that maize-French bean system is energy-efficient, economically viable and environmentally safer systems to utilize maize fallow and improve food security, may help in achieving green/circular economy. Low-cost sensors provide an opportunity to improve the spatial and temporal resolution of air quality measurements. Networks of such devices may complement the traditional air quality monitoring and provide some useful information about pollutants and their impact on health. This paper describes the network of 20 nodes for ambient PM2.5 monitoring on a campus area of Wrocław University of Science and Technology (Wrocław, Poland). Sensor nodes were equipped with optical sensors PMS A003 (Plantower), which showed high reproducibility between units. The distribution of the sensor nodes was characterised by both high density (14 devices on the main campus area) and wide spread across the city (6 devices on peripheral campuses). During the measurement campaign, signals from sensor nodes were consistent with results from regulatory monitoring stations and sensor devices were capable of indicating elevated levels of PM2.5 concentrations. A great advantage of this system was the ability to provide up-to-date air quality information to the public. Furthermore, air quality messaging was site-specific because of the observed differences in PM2.5 concentrations. N-acetylcysteine Data analysis was aimed at assessing variability between locations using Kendall's τ metric and assessing the statistical significance of the differences in measurement results from neighbouring sensor nodes using the Kolmogorov-Smirnov test. The analysis showed high importance of the nodes in the middle of the main campus and variations of signals from nodes on the peripheries. Differences in signals from sensors located in close proximity to each other were in some cases significant, but only for short-term averaged data. Nevertheless, highly visible variation in PM2.5 signals was observed in the case of nodes arranged vertically on two buildings. PM2.5 concentrations were even 2-4 times greater near the top parts of the buildings than near the ground. The effect of stratification of PM2.5 levels was observed under conditions of temperature inversion. V.To determine the water quality status of the primary tributaries in middle and lower reaches of the Yellow River Basin, water collected from the confluence of the ten tributaries and some physical, chemical and biological parameters were analyzed, and then water quality index and health risk were evaluated. Of the ten main tributaries in the middle and lower reaches, only the Qingshui River had water of medium quality in the upper reaches, while all the other tributaries contributed water of poor quality. The Jindi and Dawen rivers in the lower reaches had the poorest water quality, especially the Jindi River. TP, TN, BOD5, COD, TOC and coliform bacteria exceeded the national criteria by 155%, 1%, 97.5%, 35.5%, 114.2%, and 80%, respectively. Cluster analysis indicated that industrial, agricultural, and domestic sewage, along with industrial waste gas, were the main sources of pollution in these tributaries. An analysis of the bacterial community structure showed that the Jindi River was the most polluted and had the largest species diversity and richness of bacteria.
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