Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed-based approach.

The design of a representative surface water quality monitoring network is vital for accurately capturing the dynamics of water bodies and variability in pollution across a catchment. The representativeness of a surface water monitoring network refers to how well it reflects the characteristics of all monitored surface water bodies. In this study, using a micro-watershed-based approach, a Geographic Information System (GIS) tool (Surface Water Quality Monitoring Point Locations ANalysis (SWQM_PLAN)) has been developed to optimize the design of surface water quality monitoring networks. In the first stage of the two-stage study, a digital elevation model and minimum watershed area size were taken as input parameters and micro-watersheds with defined upstream-downstream relations were created. In the second stage, input parameters including land use data, pollution sources, and micro-watershed data, along with specific criteria, were used to identify the basins and determine the optimal locations for surface water monitoring stations. The developed GIS tool was then applied to evaluate the existing surface water monitoring network in the Gediz River Basin, designed by the Republic of Türkiye, Ministry of Agriculture and Forestry. The tool assessed the effectiveness if the existing monitoring network in terms of assessing agricultural pollution and provided potential revision suggestions to enhance the effectiveness of implemented pollution reduction measures.

Spatial mapping of greenhouse gases using a UAV monitoring platform over a megacity in China.

Urban environments are recognized as main anthropogenic contributors to greenhouse gas (GHG) emissions, characterized by unevenly distributed emission sources over the urban environments. However, spatial GHG distributions in urban regions are typically obtained through monitoring at only a limited number of locations, or through model studies, which can lead to incomplete insights into the heterogeneity in the spatial distribution of GHGs. To address such information gap and to evaluate the spatial representation of a planned GHG monitoring network, a custom-developed atmospheric sampler was deployed on a UAV platform in this study to map the CO2 and CH4 mixing ratios in the atmosphere over Zhengzhou in central China, a megacity of nearly 13 million people. The aerial survey was conducted along the main roads at an altitude of 150 m above ground, covering a total distance of 170 km from the city center to the suburbs. The spatial distributions of CO2 and CH4 mixing ratios in Zhengzhou exhibited distinct heterogeneities, with average mixing ratios of CO2 and CH4 at 439.2 ± 10.8 ppm and 2.12 ± 0.04 ppm, respectively. A spatial autocorrelation analysis was performed on the measured GHG mixing ratios across the city, revealing a spatial correlation range of approximately 2 km for both CO2 and CH4 in the urban area. Such a spatial autocorrelation distance suggests that the urban GHG monitoring network designed for emission inversion purposes need to have a spatial resolution of 4 km to characterize the spatial heterogeneities in the GHGs. This UAV-based measurement approach demonstrates its capability to monitor GHG mixing ratios across urban landscapes, providing valuable insights for GHG monitoring network design.

Contribution of emissions from the oil sands activities in Alberta, Canada to atmospheric concentration and deposition of nitrogen and sulfur species at a downwind site.

Oil sands activities in the Athabasca Oil Sands Region in Alberta, Canada, are large sources of atmospheric NOx and SO2. This study investigated the impact of oil sands emissions on the atmospheric deposition of nitrogen and sulfur species at a downwind site, about 350 km from the oil sands facilities. Measurement data are from the Canadian Air and Precipitation Monitoring Network (CAPMoN) from 2015 to 2019, including ambient concentrations of HNO3, pNO3-, NO2, pNH4+, NH3, SO2, pSO42- and base cations, as well as concentrations of NO3-, SO42-, NH4+, and base cations in precipitation. Sector analysis of air mass back trajectories was conducted to distinguish measurements with different air mass origins. Median atmospheric concentrations and dry deposition fluxes of HNO3, pNO3-, NO2, pNH4+, pSO42-, and SO2 on days when the air masses came from the oil sands sector were significantly greater than those with the "Clean" sector by 34-67%, whereas the difference in NH3 concentration was not significant. Contributions of the oil sands emissions to dry deposition fluxes of these species ranged from 3.8 to 13.1%. The precipitation-weighted mean concentrations of NO3-, SO42-, and NH4+ in samples with the oil sands sector were 76 %, 65 % and 81 % greater than those with the "Clean" sector, respectively. Contributions of the oil sands emissions to wet deposition of NO3-, SO42-, and NH4+ were 12.5 ± 8.9 %, 8.7 ± 4.4 %, and 6.0 ± 3.3 %, respectively. The annual total deposition of nitrogen and sulfur were 1.9 kg-N ha-1 and 0.74 kg-S ha-1, respectively, of which 8.0 ± 3.5 % and 8.7 ± 3.6 % were from oil sands emissions. The total deposition of sulfur and nitrogen did not exceed the critical loads (CL) of acidity, but nitrogen deposition exceeded the CLs of nutrient nitrogen in the region.

Evaluation of the water quality monitoring network layout based on driving-pressure-state-response framework and entropy weight TOPSIS model: A case study of Liao River, China.

The establishment of river water quality monitoring network is crucial for watershed protection. However, the evaluation process of monitoring network layout involves significant subjectivity and has not yet to form a complete indicator system. This study constructed an indicator system based on the DPSR (Driving-Pressure-State-Response) framework in the Liao River Basin, China. SWAT model and ArcGIS were used to quantify the indicators. And the entropy weight-TOPSIS method was employed to rank monitoring points. The results showed that pressure and state indicators had a greater impact on the network layout, with the indicator for proportion of land use in residential areas carrying the largest weight of 0.136. It suggested that the risk of river pollution remained high, and the governance strategies needed to be improved. Priority monitoring points were mainly located in the east and middle of the basin, consistent with the distribution of human activities such as urban areas and farmland. In addition, the redundancy of points should be avoided, and evaluation results should be adjusted based on the actual situation. The study provided an evaluation method for the layout of monitoring points.

5G NR launching in Greece: Preliminary in situ and monitoring network measurements of electromagnetic fields exposure levels at rooftops.

In Greece, 5G New Radio (NR) has started launching in the end of 2020, at the 3400-3800 MHz (FR1) frequency band. Focusing on 117 Base Stations (BSs) which were already equipped with 5G NR antennas, in situ broadband and frequency selective measurements have been conducted at minimum three points of interest, at adjacent rooftops (when accessible). The points have been selected according to the sweeping method and the electric field strength (E) value has been stored on the selected worst-case scenario point. Spectrum analysis was conducted in the FR1, for the allocated spectrum that corresponds to each mobile communication provider, in order to get preliminary results concerning the contribution of the 5G NR emissions in the general public exposure levels. The vast majority of the in situ measurements has been conducted in urban environments from the beginning of 2021 until the mid of 2022, since in Greece 5G NR services launching started from the big cities. Additionally, a 5G NR BS, installed in a suburban environment (in the city of Kalamata) is thoroughly investigated during its pilot and regular operation, based on broadband and frequency selective measurements data derived by the National Observatory of Electromagnetic Fields (NOEF) monitoring sensor network. In situ measurement data within the 5G NR frequency range are verified via the NOEF's output. The 5G NR contribution to the total E-field levels is assessed in time, from pilot to regular operation of the BS. In all cases, compliance with the reference levels for general public exposure is affirmed.

The cognitive reality monitoring network and theories of consciousness.

Theories of consciousness abound. However, it is difficult to arbitrate reliably among competing theories because they target different levels of neural and cognitive processing or anatomical loci, and only some were developed with computational models in mind. In particular, theories of consciousness need to fully address the three levels of understanding of the brain proposed by David Marr: computational theory, algorithms and hardware. Most major theories refer to only one or two levels, often indirectly. The cognitive reality monitoring network (CRMN) model is derived from computational theories of mixture-of-experts architecture, hierarchical reinforcement learning and generative/inference computing modules, addressing all three levels of understanding. A central feature of the CRMN is the mapping of a gating network onto the prefrontal cortex, making it a prime coding circuit involved in monitoring the accuracy of one's mental states and distinguishing them from external reality. Because the CRMN builds on the hierarchical and layer structure of the cerebral cortex, it may connect research and findings across species, further enabling concrete computational models of consciousness with new, explicitly testable hypotheses. In sum, we discuss how the CRMN model can help further our understanding of the nature and function of consciousness.

Optimal design of groundwater pollution monitoring network based on a back-propagation neural network surrogate model and grey wolf optimizer algorithm under uncertainty.

In the optimal design of groundwater pollution monitoring network (GPMN), the uncertainty of the simulation model always affects the reliability of the monitoring network design when applying simulation-optimization methods. To address this issue, in the present study, we focused on the uncertainty of the pollution source intensity and hydraulic conductivity. In particular, we utilized simulation-optimization and Monte Carlo methods to determine the optimal layout scheme for monitoring wells under these uncertainty conditions. However, there is often a substantial computational load incurred due to multiple calls to the simulation model. Hence, we employed a back-propagation neural network (BPNN) to develop a surrogate model, which could substantially reduce the computational load. We considered the dynamic pollution plume migration process in the optimal design of the GPMN. Consequently, we formulated a long-term GPMN optimization model under uncertainty conditions with the aim of maximizing the pollution monitoring accuracy for each yearly period. The spatial moment method was used to measure the approximation degree between the pollution plume interpolated for the monitoring network and the actual plume, which could effectively evaluate the superior monitoring accuracy. Traditional methods are easily trapped in local optima when solving the optimization model. To overcome this limitation, we used the grey wolf optimizer (GWO) algorithm. The GWO algorithm has been found to be effective in avoiding local optima and in exploring the search space more effectively, especially when dealing with complex optimization problems. A hypothetical example was designed for evaluating the effectiveness of our method. The results indicated that the BPNN surrogate model could effectively fit the input-output relationship from the simulation model, as well as significantly reduce the computational load. The GWO algorithm effectively solved the optimization model and improved the solution accuracy. The pollution plume distribution in each monitoring yearly period could be accurately characterized by the optimized monitoring network. Thus, combining the simulation-optimization method with the Monte Carlo method effectively addressed the optimal monitoring network design problem under uncertainty.

Reducing pest pressure and insecticide use by increasing hedgerows in the landscape.

Reducing pesticide use while maintaining agricultural production is a key challenge. Ecological theory predicts that landscape simplification is likely to increase insect pest outbreaks and limit their control by natural enemies, and this situation could boost insecticide use. Some studies have indeed detected that simpler landscapes were associated with higher insecticide use, but very few have demonstrated that this association is caused by landscape effects on pest abundance. Here, we analysed insecticide use and pest pressure in response to landscape simplification across 557 arable farms across France. Accounting for potentially confounding covariates, we found that lower cover of hedgerows in the landscape, but not semi natural areas, were associated with higher on-farm insecticide use. We also found that greater hedgerow coverage was associated with lower aphid pest pressure. Specifically, increasing the landscape-scale cover of hedgerows from 1 % to 3 % meant that insecticide use was halved. These findings suggest that restoring hedgerow cover at the landscape scale should be targeted in order to speed-up the ecological intensification of agriculture.

An optimized development of urban air quality monitoring network design based on particulate matters.

The design of an air quality monitoring network (AQMN) is the mandatory step to manage air pollution in megacities. Several studies are being done on the location selection of AQMNs based on topography, meteorology, and pollution density. Still, the critical research gap that needs to be addressed is the role of pollutants' importance and prioritization in AQMN. This study aims to utilize the sphere of influence (SOI) method to design an AQMN in a megacity based on particulate matter (PM) as the most serious urban pollutant. Model evaluation was done by employing annual emission inventory data of PM in Tabriz, an industrial and crowded megacity with high exposure to salt particulates, considering 3549 square blocks with a size of 500 m * 500 m. Then, the SOI methodology utilizing the utility function (UF) approach is applied using MATLAB software calculations to determine optimal air quality monitoring network configurations. A range of numbers of utility functions was yielded for every spot on the map. It resulted in grid city maps with final spots for PM10, PM2.5, and intersecting spots. As a result, ten sites are selected as the best possible locations for the AQMN of a 2 million population city. These results could play a precise and significant role in urban air quality decision-making and management.

Inter-comparison of measurements of inorganic chemical components in precipitation from NADP and CAPMoN at collocated sites in the USA and Canada during 1986-2019.

Wet deposition monitoring is a critical part of the long-term monitoring of acid deposition, which aims to assess the ecological impact of anthropogenic emissions of SO2 and NOx. In North America, long-term wet deposition has been monitored through two national networks: the Canadian Air and Precipitation Monitoring Network (CAPMoN) and the US National Atmospheric Deposition Program (NADP), for Canada and the USA, respectively. In order to assess the comparability of measurements from the two networks, collocated measurements have been made at two sites, one in each country, since 1986 (Sirois et al., in Environmental Monitoring and Assessment, 62, 273-303, 2000; Wetherbee et al., in Environmental Monitoring and Assessment, 1995-2004, 2010). In this study, we compared the measurements from NADP and CAPMoN instrumentation at the collocated sites at the Pennsylvania State University (Penn State), USA, from 1989 to 2016, and Frelighsburg, Quebec, Canada, from 2002 to 2019. We also included in the study the collocated daily-vs-weekly measurements by the CAPMoN network during 1999-2001 and 2016-2017 in order to evaluate the differences in wet concentration of ions due to sampling frequency alone. The study serves as an extension to two previous CAPMoN-NADP inter-comparisons by Sirois et al. (Environmental Monitoring and Assessment, 62, 273-303, 2000) and Wetherbee et al., in (Environmental Monitoring and Assessment, 1995-2004, 2010). At the Penn State University site, for 1986-2019, CAPMoN was higher than NADP for all ions, in terms of weekly concentration, precipitation-weighted annual mean concentration, and annual wet deposition. The precipitation-weighted annual mean concentrations were higher for SO42- (2%), NO3- (12%), NH4+ (16%), H+ (6%), and base cations and Cl- (11-15%). For annual wet deposition, CAPMoN was higher for SO4-2, NO3-, NH4+ and H+ (5-17%), and base cations and Cl- (12-17%) during 1986-2019. At the Frelighsburg site, NADP changed the sample collector in October 2011. For 2002-2011, the relative differences at the Frelighsburg site were positive and similar in magnitude to those at the Penn State site. For 2012-2019, the precipitation-weighted annual mean concentrations were 5-27% lower than NADP, except for H+, which was 23% higher. The change in sample collector by NADP had the largest effect on between-network biases. The comparisons of daily-vs-weekly measurements conducted by the CAPMoN network during 1999-2001 and 2016-2017 show that the weekly measurements were higher than the daily measurements by 1-3% for SO42-, NO3-, and NH4+; 3-9% for Ca2+, Mg2+, Na+, and Cl-; 10-24% for K+; and lower for H+ by 8-30% in terms of precipitation-weighted mean concentration. Thus, differences in sampling frequencies did not contribute to the systematically higher CAPMoN measurements. Understanding the biases in the data for these networks is important for interpretation of continental scale deposition models and transboundary comparison of wet deposition trends.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review.

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

Air Temperature and Relative Humidity Datasets from an Urban Meteorological Network in the City Area of Novi Sad (Serbia).

This data article describes two groups of datasets which capture, firstly - 10-minutes air temperature (Ta) and relative humidity (RH) data from 27 urban and non-urban sites over a period of 3.5 years covering 2014-2018; and secondly - hourly Ta data from 12 urban sites over a period of 2 years covering 2016 and 2017. Both datasets are from urban meteorological network located in the Novi Sad city (Serbia). These datasets have 2 different types of information in the collection: one type provides details about the monitoring sites at which the Ta and RH sensors are placed, while the second type contains Ta and RH data at all sensor locations. In all, the 10-minutes dataset contains about 185,000 instances of Ta and RH data, and the hourly datasets contain 17,544 instances of Ta data. The 10-minutes datasets were not quality controlled, but the hourly Ta data has been cleaned and gap-filled so there are 24 measures at each site for each day. There are multiple potential uses, where this data can be applied. It can provide insights in understanding intra-urban and inter-urban research, urban climate modeling on local or micro scales, heat-related public health investigations and urban environment inquiries. It can also be used in machine learning experiments, for example, to test the accuracy of classification algorithms or to build and validate spatio-temporal machine learning functions, either for classification purposes or for gap filling. These datasets are directly citable through its DOIs and available for download from the Zenodo platform or from the Fair Micromet Portal.

Optimal redesign of coastal groundwater quality monitoring networks under uncertainty: application of the theory of belief functions.

This paper presents a new methodology for the optimal redesign of water quality monitoring networks in coastal aquifers. The GALDIT index is used to evaluate the extent and magnitude of seawater intrusion (SWI) in coastal aquifers. The weights of the GALDIT parameters are optimized using the genetic algorithm (GA). A SEAWAT-based simulation model, a spatiotemporal Kriging interpolation technique, and an artificial neural network surrogate model are then implemented to simulate total dissolved solids (TDS) concentration in coastal aquifers. To obtain more precise estimations, an ensemble meta-model is developed using the Dempster-Shafer's belief function theory (D-ST) to combine the results obtained from the three individual simulation models. The combined meta-model is then used for calculating more precise TDS concentration. Some plausible scenarios are defined for variation of water elevation and water salinity at the coastline to incorporate uncertainty through the concept of value of information (VOI). Finally, the potential wells with the highest values of information are taken into consideration to redesign coastal groundwater quality monitoring network under uncertainty. The performance of the proposed methodology is evaluated by applying it to the Qom-Kahak aquifer, north-central Iran, which is threatened by SWI. At first, the individual and ensemble simulation models are developed and validated. Then, several scenarios are defined regarding the plausible changes in TDS concentration and water level at the coastline. In the next step, the scenarios, the GALDIT-GA vulnerability map, and the VOI concept are used for redesigning the existing monitoring network. The results illustrate that the revised groundwater quality monitoring network containing 10 new sampling locations outperforms the existing one based on the VOI criterion.

Proposal for a new customization process for a data-based water quality index using a random forest approach.

As the water quality index (WQI) represents water quality, it is crucial to customize the WQI for a specific purpose. In this study, to better represent water quality data using WQI, a random forest (RF) approach was used to derive the parameter weight and calculate the WQI according to the watershed and its use. Eight parameters (water temperature, dissolved oxygen, pH, electrical conductivity, suspended solids, total nitrogen, total phosphorus, and total organic carbon) were evaluated using a total of 220,103 data points collected from 900 monitoring sites throughout South Korea between 2011 and 2020. The estimation of parameter weights, key elements in developing the WQI model, was performed through the variable importance estimation method that can be derived from the RF model. The parameter weights were derived based on various spatiotemporal datasets, and it was confirmed that the spatiotemporal differences in weights according to data characteristics represented the regional and seasonal water quality characteristics. Consequently, a customized WQI representing water quality characteristics could be calculated using data-based weights, and it is expected that a data-based customized WQI could be developed to better match the previous WQI to the purpose and target source.

A hybrid deep learning model for regional O3 and NO2 concentrations prediction based on spatiotemporal dependencies in air quality monitoring network.

Short-term prediction of urban air quality is critical to pollution management and public health. However, existing studies have failed to make full use of the spatiotemporal correlations or topological relationships among air quality monitoring networks (AQMN), and hence exhibit low precision in regional prediction tasks. With this consideration, we proposed a novel deep learning-based hybrid model of Res-GCN-BiLSTM combining the residual neural network (ResNet), graph convolutional network (GCN), and bidirectional long short-term memory (BiLSTM), for predicting short-term regional NO2 and O3 concentrations. Auto-correlation analysis and cluster analysis were first utilized to reveal the inherent temporal and spatial properties respectively. They demonstrated that there existed temporal daily periodicity and spatial similarity in AQMN. Then the identified spatiotemporal properties were sufficiently leveraged, and monitoring network topological information, as well as auxiliary pollutants and meteorology were also adaptively integrated into the model. The hourly observed data from 51 air quality monitoring stations and meteorological data in Shanghai were employed to evaluate it. Results show that the Res-GCN-BiLSTM model was better adapted to the pollutant characteristics and improved the prediction accuracy, with nearly 11% and 17% improvements in mean absolute error for NO2 and O3, respectively compared to the best performing baseline model. Among the three types of monitoring stations, traffic monitoring stations performed the best for O3, but the worst for NO2, mainly due to the impacts of intensive traffic emissions and the titration reaction. These findings illustrate that the hybrid architecture is more suitable for regional pollutant concentration.

A monitoring and data analysis method for microplastics in marine sediments.

In Europe, policy frameworks demand the monitoring of microplastics in marine sediments. Here we provide a monitoring and data analysis method for microplastic particles designed to be used in the context of Marine Strategy Framework Directive (MSFD) and OSPAR policy frameworks. Microplastics were analysed in marine sediments at four different locations in Dutch coastal and transitional waters using replicate sampling to investigate micro-spatial variation. Particle size distribution followed a power law with slope 3.76. Thirteen polymers were identified, with their composition varying between sediments near densely populated West coast areas versus the more rural Wadden Sea area. We quantify differences in the micro-spatial variation of microplastic concentrations between locations using the relative standard error of the mean (RSEM). This metric provides an opportunity to optimize the sensitivity of trend detection in microplastic monitoring networks by selecting locations with relatively low micro-spatial variation. We provide a method to optimize the number of replicate samples for a given location using its relationship with the RSEM. Two replicate samples appear to be cost-effective for relatively homogenous locations, whereas more heterogenous locations require four replicates.

Where to measure water quality ? Application to nitrogen pollution in a catchment in France.

Information on the water quality of rivers can be used to judge the effectiveness of past policies or to guide future environmental policies. Consequently, the location of water quality monitoring stations (WQMSs) plays an important role in river pollution control. In the 2000s, a literature developed on the optimization of WQMS location to identify pollution hot spots, average quality, or to minimize the detection time of a potential source of accidental pollution. This article is part of a new literature aimed at locating WQMSs in order to optimize the economic value of information (EVOI) generated by water quality monitoring networks (WQMNs). The field of study is a catchment in northeastern France where the purpose of quality measurement is to define a policy of reduction of agricultural nitrogen fertilizers in order to reach the standard of 50 mg/l of nitrate at the WQMS. Agro-hydrological and economic models estimate the net benefit of input reduction depending on the location of the WQMS on the basis of different assumptions concerning the ecological damage generated by nitrate. We show that the magnitude of the ecological damage and, consequently, the perception of the contamination generated by nitrate in water, play a decisive role on the optimal location of the WQMS, as well as on the benefit of the economic optimization of locations, compared to traditional optimization. Locating WQMSs in a way that maximizes EVOI will be more attractive for very high or very low levels of damage. However, in this context, linking damage to nitrate concentration or to concentration coupled with riparian population density alone will have little impact.

An advection-reaction model of emerging contaminants in ground and surface waterbodies with known topology: a data-based approach.

A simple data-based advection-reaction (reactive transport) model applicable to both rivers and aquifers monitoring networks is proposed. It is built on (a) available monitoring data, and (b) graph-theoretical concepts, specifically making use of the Laplacian matrix to capture the network topology and the advection process. The method yields useful information regarding the dynamic spatial behavior of the variables monitored, expressed in terms of quantitative parameters like characteristic length, entropy, first-order decay constants, synchronization between sites, and the external inputs/outputs to the system. The model was tested in an unconfined shallow aquifer located in the lower Besòs River (Spain), in which 37 pharmaceutical compounds were monitored at 7 sites, alongside two campaigns (February and May 2021). Characteristic lengths were, on average, of the same order (24.5 m) as the mean distance between consecutive monitoring sites (33.6 m), thus reflecting an adequate monitoring network design. From an estimated mean advection velocity (0.24 m·h-1), first-order decay constants were calculated for each compound and campaign, with mean values of 0.025 h-1 (February) and 0.005 h-1 (May). Whereas entropy was generally slightly larger values in February than in May (mean values of 1.02 and 0.9 entropy units respectively), synchronization showed the opposite trend (mean values of 62.4% and 68.8% respectively). The input/output profiles were generally site-dependent, regardless of the compound, and campaign considered. • A new advection-reaction modeling approach directly based on experimental data obtained from monitoring campaigns together with the network topology is proposed. • The method yields new quantitative information regarding the dynamic behavior of the variables monitored, useful for both research and management purposes.

Impact of COVID-19 lockdown on ambient noise levels in seven metropolitan cities of India.

The paper analyzed the impact of lockdown on the ambient noise levels in the seventy sites in the seven major cities of India and ascertained the noise scenario in lockdown period, and on the Janta Curfew day in comparison to the pre-lock down period and year 2019 annual average values. It was observed that the majority of the noise monitoring sites exhibited a decrement in ambient day and night equivalent noise levels on the national Janta Curfew day and Lockdown period as compared with the normal working days attributed to the restricted social, economical, industrial, urbanization activity and reduced human mobility. A mixed pattern was observed at a few sites, wherein the ambient day and night equivalent noise levels during Janta curfew day and Lockdown period had been reported to be higher than that on the normal working days. The study depicts the noise scenario during the lockdown and pre-lockdown period for seventy sites in India and shall be instrumental in analyzing the consequences and implications of imposing lockdowns in future on the environmental noise pollution in Indian cities.

[Collaborative monitoring network of ecologically fragile areas in China and its application in carrying capacity research]. / ä¸­å›½ç”Ÿæ€è„†å¼±åŒºè”ç½‘ååŒè§‚æµ‹åŠå ¶åœ¨æ‰¿è½½åŠ›ç ”ç©¶ä¸­çš„åº”ç”¨.

Ecologically fragile areas account for more than 60% of land area in China. Global change and human activities are aggravating ecosystem degradation and reducing the carrying capacity of resources and environment. It is important to accurately quantify the carrying capacity of resources and environment in ecologically fragile areas to deal with the risk and challenge of global change and to speed up the construction of ecological civilization. How-ever, existing methods evaluating carrying capacity of resources and environment are difficult to reflect the transmission effect of ecosystem structures, processes and functions changes among resource, environment and carrying capacity. Therefore, it is essential to establish a field observation network and obtain the comprehensive data set of resource and environment elements-ecosystem structure, function and process-ecosystem carrying capacity for develo-ping the theory and evaluation method. We introduced the collaborative monitoring networks of flux and UAV photographing, including the thoughts, practice, and preliminary results in the study of ecosystem structure, process and function in the fragile ecosystems of China. Based on the achievements and progress, we proposed the application of collaborative monitoring networks in capacity evaluation.