Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O₃, NO₂, NO, PM₂.₅, and PM₁₀, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

Linking water to research on coupled human and natural systems (CHANS) has attracted wide interest as a means of supporting human-natural sustainability. However, most current research does not focus on water environmental properties; instead, it is at the stage of holistic status assessment and measures adjustment from the point of view of the whole study region without revealing the dynamic interaction between human activities and natural processes. This paper establishes an integrated model that combines a System Dynamics model, a Cell Automaton model and a Multiagent Systems model and exploits the potential of the combined model to reveal regions' human-water interaction status during the process of urban evolution, identify the main pollution sources and spatial units, and provide the explicit space-time measurements needed to enhance local human-natural sustainability. The successful application of the integrated model in the case study of Changzhou City, China reveals the following. (1) As the city's development has progressed, the water environment status in some spatial units is still unsatisfactory and may even become more serious, especially in the urban areas of the Urban District and Liyang County. The concentration of Chemical Oxygen Demand (COD) in monitoring section 157 of the Urban District has increased from 36.90 mg/l to 40.84 mg/l. The main source of this increase is the increase in secondary industry. (2) With the application of the spatially explicit measures of the sewage treatment ratio improvement and new sewage plant construction, the water quality in the urban area has significantly improved and now satisfies the water quality standards. The measure of livestock manure utilization enhancement is adopted to improve the spatial units in which livestock is the main pollution source and achieve the goal of water quality improvement. The model can be used to support the sustainable status assessment of human-water interaction and to identify effective measures that can be used to realize human-water sustainability along with social-economic development.

Floods are threats to ecosystems that are caused by natural disasters such as typhoons and heavy rain, and to respond to these threats, resilience needs to be improved. In this study, the response of the social-ecological system of Haeundae-gu (Busan, Republic of Korea) to disasters is analyzed by using a causal loop diagram, and a resilience improvement plan is presented by simulating the disaster resilience using green infrastructure through the System Resilience Dynamics Model. First, the resilience values are highest when green infrastructure is applied at the maximum applicable ratio (30%) compared with no application. Second, in the public and private areas of Haeundae-gu, resilience according to green roof scenario was higher until approximately 8 h after the beginning of rainfall, but then the resilience according to infiltration storage facility scenario was higher. In the transportation and industrial areas, the overall resilience according to infiltration storage facility scenario was higher than the resilience according to porous pavement scenario. This study demonstrates that a resilience improvement plan based on simulation can support decision making to respond to disasters such as typhoons.

Hazard assessment of chemical contaminants often relies on short term or partial life-cycle ecotoxicological tests, while the impact of low dose throughout the entire life cycle of species across multiple generations has been neglected. This study aimed at identifying the individual and population-level consequences of chronic water contamination by environmental concentrations of three organic micropollutants, ibuprofen, bisphenol A and benzo[a]pyrene, on Aedes aegypti mosquito populations in experimental conditions. Life-history assays spanning the full life-cycle of exposed individuals and their progeny associated with population dynamics modelling evidenced life-history traits alterations in unexposed progenies of individuals chronically exposed to 1 μg/L ibuprofen or 0.6 μg/L benzo[a]pyrene. The progeny of individuals exposed to ibuprofen showed an accelerated development while the progeny of individuals exposed to benzo[a]pyrene showed a developmental acceleration associated with an increase in mortality rate during development. These life-history changes due to pollutants exposure resulted in relatively shallow increase of Ae. aegypti asymptotic population growth rate. Multigenerational exposure for six generations revealed an evolution of population response to ibuprofen and benzo[a]pyrene across generations, leading to a loss of previously identified transgenerational effects and to the emergence of a tolerance to the bioinsecticide Bacillus turingiensis israelensis (Bti). This study shed light on the short and long term impact of environmentally relevant doses of ibuprofen and benzo[a]pyrene on Ae. aegypti life-history traits and insecticide tolerance, raising unprecedented perspectives about the influence of surface water pollution on vector-control strategies. Overall, our approach highlights the importance of considering the entire life cycle of organisms, and the necessity to assess the transgenerational effects of pollutants in ecotoxicological studies for ecological risk assessment. Finally, this multi-generational study gives new insight about the influence of surface water pollution on microevolutionary processes.

A dynamic model of forest ecosystems was used to investigate the effects of climate change, atmospheric deposition and harvest intensity on 48 forest sites in Sweden (n = 16) and Switzerland (n = 32). The model was used to investigate the feasibility of deriving critical loads for nitrogen (N) deposition based on changes in plant community composition. The simulations show that climate and atmospheric deposition have comparably important effects on N mobilization in the soil, as climate triggers the release of organically bound nitrogen stored in the soil during the elevated deposition period. Climate has the most important effect on plant community composition, underlining the fact that this cannot be ignored in future simulations of vegetation dynamics. Harvest intensity has comparatively little effect on the plant community in the long term, while it may be detrimental in the short term following cutting. This study shows: that critical loads of N deposition can be estimated using the plant community as an indicator; that future climatic changes must be taken into account; and that the definition of the reference deposition is critical for the outcome of this estimate.

Simulation modelling with CHUM-AM was carried out to investigate the accumulation and release of atmospherically-deposited heavy metals (Ni, Cu, Zn, Cd and Pb) in six moorland catchments, five with organic-rich soils, one with calcareous brown earths, in the Pennine chain of northern England. The model considers two soil layers and a third layer of weathering mineral matter, and operates on a yearly timestep, driven by deposition scenarios covering the period 1400-2010. The principal processes controlling heavy metals are competitive solid-solution partitioning of solutes, chemical interactions in solution, and chemical weathering. Agreement between observed and simulated soil metal pools and surface water concentrations for recent years was generally satisfactory, the results confirming that most contemporary soil metal is from atmospheric pollution. Metals in catchments with organic-rich soils show some mobility, especially under more acid conditions, but the calcareous mineral soils have retained nearly all anthropogenic metal inputs. Complexation by dissolved organic matter and co-transport accounts for up to 80% of the Cu in surface waters.

Tetracyclines (TCs) are frequently detected in agricultural soils worldwide, causing a potential threat to crops and human health. In this study, diffusive gradients in thin films technique (DGT) was used to measure the distribution and exchange rates of three TCs (tetracycline (TC), oxytetracycline (OTC) and chlortetracycline (CTC)) between the solid phase and solution in five farmland soils. The relationship between the accumulated masses with time suggested that TCs consumption in soil solution by DGT would induce the supply from the soil solid phase. The distribution coefficient for the labile antibiotics (Kdl), response time (Tc) and desorption/adsorption rates (kb and kf) between dissolved and sorbed TCs were derived from the dynamic model of DIFS (DGT induced fluxes in soils). The Kdl showed similar sizes of labile solid phase pools for TC and OTC while larger pool sizes were observed for CTC in the soils. Although the concentrations of CTC were lowest in soil solution, the potential hazard caused by continuous release from soil particles could not be ignored. The long response time (>30 min in most cases) suggested that the resupply of TCs from soil solids was limited by their desorption rates (1.26-121 × 10−6 s−1). The soils in finer texture, with higher clay and silt contents (<50 μm) showed a greater potential for TCs release.

Thiamethoxam is a widely used pesticide applied to different field crops. To inform risk assessment for this pesticide across relevant crops, we usually rely on field trials, which require time, costs and energy. For providing reliable data across crops and reduce experimental efforts, field trials should be complemented with dynamic modelling. In the present work, we hence focused on combining field trials with dynamic modelling to simulate mass evolutions of the pesticide-plant-system for thiamethoxam applied to wheat, lettuce and tomato as three major food crops. Field trials were conducted with QuEChERS (quick, easy, cheap, effective, rugged and safe) liquid chromatography-mass spectrometry, which gave consistent maximum residue concentrations for thiamethoxam in wheat, lettuce and tomato. We used these residues to evaluate the related dietary risk of humans consuming these food crops. Our results indicated that thiamethoxam did not provide any unacceptable dietary risk for humans across these three food crops, which is in line with findings from previous studies. Results for the studied crops could be extrapolated to other crops and with that, our study constitutes a cost- and time-efficient way of providing reliable input for risk assessment of pesticides across crops, which is relevant for both practitioners and regulators.

This review evaluates the three dynamic models (biokinetic model: BK, physiologically based pharmacokinetic model: PBPK, and toxicokinetic-toxicodynamic model: TKTD) in our understanding of the key questions in metal ecotoxicology in aquatic systems, i.e., bioaccumulation, transport and toxicity. All the models rely on the first-order kinetics principle of metal uptake and elimination. The BK model basically treats organisms as a single compartment, and is both physiologically and geochemically based. With a good understanding of each kinetic parameter, bioaccumulation of metals in any aquatic organisms can be studied holistically and mechanistically. Modeling efforts are not merely restrained from the prediction of metal accumulation in the tissues, but instead provide the direction of the key processes that need to be addressed. PBPK is more physiologically based since it mainly addresses the transportation, transformation and distribution of metals in the organisms. It can be treated conceptually as a multi-compartmental kinetic model, whereas the physiology is driving the development of any good PBPK model which is no generic for aquatic animals and contaminants. There are now increasingly applications of the PBPK modeling specifically in metal studies, which reveal many important processes that are impossible to be teased out by direct experimental measurements without adequate modeling. TKTD models further focus on metal toxicity in addition to metal bioaccumulation. The TK part links exposure and bioaccumulation, while the TD part links bioaccumulation and toxic effects. The separation of TK and TD makes it possible to model processes, e.g., toxicity modification by environmental factors, interaction between different metals, at both the toxicokinetic and toxicodynamic levels. TKTD models provide a framework for making full use of metal toxicity data, and thus provide more information for environmental risk assessments. Overall, the three models reviewed here will continue to provide guiding principles in our further studies of metal bioaccumulation and toxicity in aquatic organisms.

China is now experiencing major public health challenges caused by air pollution. Few studies have quantified the dynamics of air pollution and its impact on the risk of respiratory infection. We conducted an integrated data analysis to quantify the association among air quality index (AQI), meteorological variables and respiratory infection risk in Shaanxi province of China in the period of November 15th, 2010 to November 14th, 2016. Our analysis illustrated a statistically significantly positive correlation between the number of influenza-like illness (ILI) cases and AQI, and the respiratory infection risk has increased progressively with increased AQI with a time lag of 0–3 days. We also developed mathematical models for the AQI trend and respiratory infection dynamics, incorporating AQI-dependent incidence and AQI-based behaviour change interventions. Our combined data and modelling analysis estimated the basic reproduction number for the respiratory infection during the studying period to be 2.4076, higher than the basic reproduction number of the 2009 pandemic influenza in the same province. Our modelling-based simulations concluded that, in terms of respiratory infection risk reduction, the persistent control of emission in the China's blue-sky programme is much more effective than substantial social-economic interventions implemented only during the smog days.