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The novel coronavirus (COVID-19) originated in Wuhan city of China in late December 2019 and affected the (atmosphere and hydrosphere) and also impacted economic activities due to the lockdown. Several studies have reported the impact of the COVID-19 pandemic on significant changes in the air quality index, but only a few studies have reported the relationship between water quality parameters and COVID-19. The findings of the current study revealed that the mean values of pH in the Yamuna river were reported to be 7.77 ± 0.31, 7.55 ± 0.40, and 7.31 ± 0.44 for pre-, during, and post-pandemic periods. The changes in the pH values indicated that Yamuna water quality became less alkaline due to restricted industrial activities along the river during the lockdown period. Further, the mean values of COD were observed to be 60.57 ± 16.79, 62.99 ± 23.17, and 129.06 ± 36.96; the BOD values were 18.20 ± 8.42, 20.16 ± 5.22, and 33.35 ± 10.35, and DO values were 3.30 ± 1.15, 3.20 ± 0.96, and 3.49 ± 1.56 respectively for pre-, during, and post-pandemic periods. The results of these parameters indicated that agriculture, including local source discharges, was a major factor affecting water quality parameters during the pandemic period. According to the study, there is a significant positive association between the BOD and COD with values 0.99, 0.98, and 0.94, respectively, whereas a strong negative correlation was calculated between DO, COD, BOD for pre-, during, and post-pandemic periods. This study would be enlightening among the scientists, researchers, and government to address the water issues along with policy formulation. [ABSTRACT FROM AUTHOR]

Accurate estimation of traffic emissions and analysis of spatio-temporal distribution on urban roads play a crucial role in the development of low-carbon transportation system. Traditionally, a region's emission characteristics have been studied using numerous emission models with GPS-based spatio-temporal data. Due to the heavy data processing needs of GPS-based data, emission characteristics for a large region have been studied by dividing the region into a limited number of smaller areas or units. Additionally, GPS data are based on a few vehicles in the traffic which does not fully reflect road conditions. This paper proposed an approach that can be used to study and calculate the spatio-temporal emission pattern of a region at a roadway section level by using Baidu's online traffic data and COPERT model. The proposed method can be used to estimate road-level emission patterns while avoiding the impact of redundant data in large datasets, making the dataset more reliable, applicable, and scalable. The proposed approach has been demonstrated through a study of spatio-temporal emission patterns in the Qiaoxi district within city of Shijiazhuang, China. Online data crawling technology was used to obtain data on urban road traffic speed and driving distance. The linear reference technology was used to construct a two-layer road network model to conduct the coupling and matching of traffic data with the road network data. The COPERT model was implemented to calculate the average traffic emissions on each road in the road network, and a traffic emission intensity index was proposed to quantify the CO, VOC, NOx and CO2 emissions on urban roads in the study area. The analysis results show that the traffic emission intensity of the expressway, trunk road, secondary road, and branch road is high during the morning peak (7 AM-9 AM) and evening peak (5 PM-7 PM). The sections with higher traffic emission intensity are mainly concentrated on the main roads and secondary roads such as Jiefang South Street, Shitong Road and Xinhua Road. Nearly one-third of 2nd Ring and 3rd Ring roads also have relatively high emission intensity. The research results provide new ideas for estimating traffic emissions in urban road networks and analyzing the spatio-temporal distribution of traffic emissions. The research results can also provide a decision-making basis for traffic management departments to formulate energy-saving and emission-reduction measures and promote the development of urban green and low-carbon transportation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Ren et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)