Decision-making related to nitrogen (N) fertilization is a crucial step in agronomic practices because of its direct interactions with agronomic productivity and environmental risk. Here, we hypothesized that soil apparent N balance could be used as an indicator to determine the thresholds of N input through analyzing the responses of the yield and N loss to N balance. Based on the observations from 951 field experiments conducted in rice (Oryza sativa L.) cropping systems of China, we established the relationships between N balance and ammonia (NH₃) volatilization, yield increase ratio, and N application rate, respectively. Dramatical increase of NH₃ volatilizations and stagnant increase of the rice yields were observed when the N surplus exceeded certain levels. Using a piecewise regression method, the seasonal upper limits of N surplus were determined as 44.3 and 90.9 kg N ha⁻¹ under straw-return and straw-removal scenarios, respectively, derived from the responses of NH₃ volatilization, and were determined as 53.0–74.9 and 97.9–112.0 kg N ha⁻¹ under straw-return and straw-removal scenarios, respectively, derived from the maximum-yield consideration. Based on the upper limits of N surplus, the thresholds of N application rate suggested to be applied in single, middle-MLYR, middle-SW, early, and late rice types ranged 179.0–214.9 kg N ha⁻¹ in order to restrict the NH₃ volatilization, and ranged 193.3–249.8 kg N ha⁻¹ in order to achieve the maximum yields. If rice straw was returned to fields, on average, the thresholds of N application rate could be theoretically decreased by 17.5 kg N ha⁻¹. This study provides a robust reference for restricting the N surplus and the synthetic fertilizer N input in rice fields, which will guide yield goals and environmental protection.

Offshore human activities lead to increasing amounts of underwater noise in coastal and shelf environments, which may affect commercially-important benthic invertebrate groups like the re-stocked Helgoland European lobster (Homarus gammarus) in the German Bight (North Sea). It is crucial to understand the impact tonal low-frequency noises, like maritime transport and offshore energy operations, may have on substrate choice and lobsters' behavior to assess potential benefits or bottlenecks of new hard-substrate artificial offshore environments that become available. In this study, we investigated the full factorial effect of a tonal low-frequency noise and predator presence on young-of-year (YOY) European lobsters' in a diurnal and nocturnal experiment. Rocks and European oyster shells (Ostrea edulis) were offered as substrate to YOY lobsters for 3 h. Video recordings (n = 134) allowed the identification of lobsters' initial substrate choice, diel activity and key behaviors (peeking, shelter construction, exploration and hiding). To ensure independence, YOY lobsters in the intermolt stage were randomly selected and assigned to the experimental tanks and used only once. We provide the first evidence that stressors alone, and in combination, constrain YOY lobsters' initial substrate choice towards rocks. During nighttime, the joint effect of exposure to a constant low-frequency noise and predator presence decreased antipredator behavior (i.e., hiding) and increased exploration behavior. Noise may thus interfere with YOY lobsters' attention and decision-making processes. This outcome pinpoints that added tonal low-frequency noise in the environment have the potential to influence the behavior of early-life stages of European lobsters under predator pressure and highlights the importance of including key benthic invertebrates' community relationships in anthropogenic noise risk assessments. Among others, effects of noise must be taken into consideration in plans involving the multi-use of any offshore area for decapods’ stock enhancement, aquaculture, and temporary no-take zones.

Environmental complexity leads to differences in the spatial distribution of heavy metal pollution in soil and rice. Such spatial differences will seriously affect the safety of planted rice and can impact regional management and control. How to scientifically reveal these spatial differences is an urgent problem. In this study, the spatial mismatch relationship between Cd pollution in soil and rice grains (brown rice) was first explored by the interpolation method. To further reveal the causes of these, the specific recognition rules of the spatial relationship of Cd pollution were extracted based on a decision tree model, and the results were mapped. The results revealed a spatial mismatch in Cd pollution between the soil and rice grains in the study area, and the main results are as follows: (i) slight soil pollution and safe rice accounted for 68.88% of the area; (ii) slight soil pollution and serious rice pollution accounted for 13.39% of the area and (iii) safe soil and serious rice pollution accounted for 11.63% of the area. In addition, 11 recognition rules of Cd spatial pollution relationship between soil and rice were proposed, and the main environmental factors were determined: SOM (soil organic matter), Dis-residence (distance from residential area), soil pH and LAI (leaf area index). The average accuracy of rule recognition was 75.90%. The study reveals the spatial mismatch of heavy metal pollution in soil and crops, providing decision-making references for the spatial accurate identification and targeted prevention of heavy metal pollution spaces.

In the last decade many studies have described the ingestion of plastic in marine animals. While most studies were dedicated to understanding the pre-ingestion processes involving decision-making foraging choices based on visual and olfactory cues of animals, our knowledge in the post-ingestion consequences remains limited. Here we proposed a theoretical complementary view of post-ingestion consequences, attempting to connect plastic ingestion with plastic-induced satiety. We analyzed data of plastic ingestion and dietary information of 223 immature green turtles (Chelonia mydas) from tropical Brazilian reefs in order to understand the impacts of plastic ingestion on foraging behavior. Generalized linear mixing models and permutational analysis of variance suggested that plastic accumulations in esophagus, stomach and intestine differed in their impact on green turtle’s food intake. At the initial stages of plastic ingestion, where the plastic still in the stomach, an increase in food intake was observed. The accumulation of plastic in the gastrointestinal tract can reduce food intake likely leading to plastic-induced satiety. Our results also suggest that higher amounts of plastics in the gastrointestinal tract may led to underweight and emaciated turtles. We hope that adopting and refining our proposed framework will help to clarify the post-ingestion consequences of plastic ingestion in wildlife.

Inhalation exposure to atmospheric polycyclic aromatic hydrocarbons (PAHs) is posing a great threat to human health. Biomass combustion in rural areas contributes greatly to the total PAH emission in China. To conduct a comprehensive risk assessment of ambient PAHs in rural China, a nationwide air sampling campaign was carried out in this study. The 16 U.S. Environmental Protection Agency priority PAHs in tree bark, which was employed as a passive air sampler, were analyzed. The summation of the 16 PAHs ranged from 11.7 to 12,860 ng/m³ in the air of rural China. The national median benzo(a)pyrene equivalent (BaPₑq) concentration was 18.4 ng/m³, with the range from 0.334 to 2497 ng/m³. The total inhalation carcinogenic risks of individual PAHs, with the exception for naphthalene, were very low (<1 × 10⁻⁶) at most of the sampling sites. The national median excess lifetime lung cancer risk associated with inhalation exposure to atmospheric PAHs was 20.3 × 10⁻⁶, corresponding to a population attributable fraction (PAF) of 3.38‰. Our estimations using tree bark were comparable to those reported in other studies and the uncertainties of the variables in the dataset were within the acceptable levels, demonstrating that tree bark is feasible for assessing the atmospheric PAH pollution and associated health risks. We feel that the outputs from this study can assist decision-makers focusing on protecting human health against exposure to atmospheric PAHs in rural China.

From a health prospective, it is critical to provide a comprehensive model which integrates all the parameters involved in virus transmission and its consequences on human body. In order to estimate the health risks, for workers and residents, associated with an exposure airborne viruses emitted from a wastewater treatment (WWTP), the concentration levels of viruses in emitted bioaerosols over a twelve-month period were measured by real-time polymerase chain reaction (RT-PCR). A combined Gaussian plum dispersion model and quantitative microbial risk assessment (QMRA) with Monte-Carlo simulation served as suitable explanatory tools to estimate the risk of acquiring gastrointestinal illness (GI) due to exposure to air containing Rotavirus (RoV) and Norovirus (NoV) bioaerosols. Additionally, DALY metric was applied to quantify the disability and mortality for workers and residents. RoV and NoV were detected above aeration tank with annual mean concentration 27 and 3099 (Viruses/m³.h), respectively. The medium calculated DALY indicator based on viral loads in contaminant source (RoV:5.76 × 10⁻² and NoV:1.23 × 10⁻¹) and estimated in different distances away (300–1000 m) (RoV:2.87 × 10⁻²- 2.75 × 10⁻² and NoV:1.14 × 10⁻¹-1.13 × 10⁻¹) were markedly higher than the threshold values recommended by US EPA (10⁻⁴ DALY pppy) and WHO (10⁻⁶ DALY pppy). The sensitivity analysis highlighted dose exposure and disease burden per case (DBPC) as two most influential factors for both workers and residents following exposure to two pathogens of concern. Due to high resistance and high concentration in the environment, the presence of RoV and NoV can intensify the consequences of diarrhea especially for children under five years of age; A comprehensible and transparent presentation of DALYs and QMRA can help decision makers and responsibilities to justify the priorities of exposure to wastewater in comparison with other risks of daily life.

Plastic pollution is ubiquitous throughout the marine environment, with microplastic (i.e. <5 mm) contamination a global issue of emerging concern. The lack of universally accepted methods for quantifying microplastic contamination, including consistent application of microscopy, photography, an spectroscopy and photography, may result in unrealistic contamination estimates. Here, we present and apply an analysis workflow tailored to quantifying microplastic contamination in marine waters, incorporating stereomicroscopic visual sorting, microscopic photography and attenuated total reflectance (ATR) Fourier transform infrared (FTIR) spectroscopy. The workflow outlines step-by-step processing and associated decision making, thereby reducing bias in plastic identification and improving confidence in contamination estimates. Specific processing steps include (i) the use of a commercial algorithm-based comparison of particle spectra against an extensive commercially curated spectral library, followed by spectral interpretation to establish the chemical composition, (ii) a comparison against a customised contaminant spectral library to eliminate procedural contaminants, and (iii) final assignment of particles as either natural- or anthropogenic-derived materials, based on chemical type, a compare analysis of each particle against other particle spectra, and physical characteristics of particles. Applying this workflow to 54 tow samples collected in marine waters of North-Western Australia visually identified 248 potential anthropogenic particles. Subsequent ATR-FTIR spectroscopy, chemical assignment and visual re-inspection of photographs established 144 (58%) particles to be of anthropogenic origin. Of the original 248 particles, 97 (39%) were ultimately confirmed to be plastics, with 85 of these (34%) classified as microplastics, demonstrating that over 60% of particles may be misidentified as plastics if visual identification is not complemented by spectroscopy. Combined, this tailored analysis workflow outlines a consistent and sequential process to quantify contamination by microplastics and other anthropogenic microparticles in marine waters. Importantly, its application will contribute to more realistic estimates of microplastic contamination in marine waters, informing both ecological risk assessments and experimental concentrations in effect studies.

Urban water pollution poses risks of waterborne infectious diseases. Therefore, in order to improve urban liveability, effective pollution mitigation strategies are required underpinned by predictions generated using water quality models. However, the lack of reliability in current modelling practices detrimentally impacts planning and management decision making. This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate indicators of human health risk, namely, turbidity, total nitrogen and fats/oils. Expert knowledge was found to be of critical importance in assessing the interdependent relationships between health risk indicators and influential factors. The spatial variability maps of health risk indicators developed enabled the initial identification of high risk areas in which flooding was found to be the most significant influential factor in relation to human health risk. Surprisingly, population density was found to be less significant in influencing health risk indicators. These high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. It was evident that decision making in relation to the design of pollution mitigation strategies needs to account for the impact of landscape characteristics on water quality, which can be related to risk to human health.

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.

This study evaluates the in-situ use of field portable X-ray Fluorescence (pXRF) for metal-contaminated site assessments, and assesses the advantages of increased sampling to reduce risk, and increase confidence of decision making at a lower cost. Five metal-contaminated sites were assessed using both in-situ pXRF and ex-situ inductively coupled plasma mass spectrometry (ICP–MS) analyses at various sampling resolutions. Twenty second in-situ pXRF measurements of Mn, Zn and Pb were corrected using a subset of parallel ICP–MS measurements taken at each site. Field and analytical duplicates revealed sampling as the major contributor (>95% variation) to measurement uncertainties. This study shows that increased sampling led to several benefits including more representative site characterisation, higher soil-metal mapping resolution, reduced uncertainty around the site mean, and reduced sampling uncertainty. Real time pXRF data enabled efficient, on-site decision making for further judgemental sampling, without the need to return to the site. Additionally, in-situ pXRF was more cost effective than the current approach of ex-situ sampling and ICP–MS analysis, even with higher sampling at each site. Lastly, a probabilistic site assessment approach was applied to demonstrate the advantages of integrating estimated measurement uncertainties into site reporting.