Environmental Sets Research Articles

Emission taxes, firm relocation, and product differentiation

This paper studies the effect of an emission tax on the relocation decision in a duopoly with vertical product differentiation. We establish the relationship between an exogenous product quality markup, relocation cost, and emission taxation in a two-country-setting for three cases: (a) an environmental tax set only by one country, (b) non-cooperative environmental taxation in both countries, and (c) coordinated environmental taxation. We show that a larger product quality markup and, thus, weaker competition can serve as a substitute for environmental policy as both reduce emissions. However, weaker competition makes firm relocation more likely, which results in emission shifting instead of emission reduction. The higher the product quality markup, the more likely it is that at least one firm relocates to the foreign country. Emission taxation in the foreign country changes location decisions: If also the foreign country applies an emission tax, at least one firm stays in the home country. If both governments set taxes non-cooperatively, the low-quality firm always stays in the home country. If both countries set taxes cooperatively, both firms are more likely to stay in the home country. However, relocation of the low-quality firm is a possible outcome under cooperative taxation.

A visualization tool for citizen-science marine debris big data

ABSTRACT We describe the design and structure of a web-based visualization tool for an Australian marine debris database and its application in environmental research, management and science communication. We give examples of its use in generating hypotheses regarding processes driving the distribution of marine debris, identifying source reduction opportunities and communicating science to the public and stakeholders. We suggest this as a model for utilizing other latent environmental data sets, enabling users to implement the five ‘source-to-sea’ steps to characterize, engage, diagnose, design, act and adapt when addressing leading environmental concerns.

Matching methods to quantify wildfire effects on forest carbon mass in the U.S. Pacific Northwest.

Forest wildfires consume and redistribute carbon within forest carbon pools. Because the incidence of wildfires is unpredictable, quantifying wildfire effects is challenging due to the lack of prefire data or controls from experiments over a large landscape. We explored a quasi-experimental method, propensity score matching, to estimate wildfire effects on aboveground forest woody carbon mass in Washington and Oregon, United States. Observational data, including national forest inventory plot measurements and satellite imagery metrics, were utilized to obtain a control set of unburned plots that are comparable to burned plots in terms of environmental conditions as well as spatial locations. Three matching methods were implemented: propensity score matching (PSM), spatial matching (SM), and distance-adjusted propensity score matching (DAPSM). We investigated if propensity score matching with and without spatial adjustment led to different outcomes in terms of (1) balance in covariate distributions between burned and control plots, (2) mean carbon mass obtained from the selected control plots compared to burned and all unburned plots, and (3) estimates of wildfire effects by burn severity. We found that PSM and SM, which use only the environmental covariate set or the spatial distance for estimating propensity scores, respectively, did not appear to produce a comparable set of control plots in terms of the estimated propensity scores and the outcomes of mean carbon mass. DAPSM was the preferred method both in balancing the observed covariates and in dealing with unobservable confounding variables through spatial adjustment. The average wildfire effects estimated by DAPSM showed clear evidence of redistribution of carbon among aboveground woody pools, from live to dead trees, but the consumption of total woody carbon by wildfire was not substantial. Only moderate burn severity led to significant reduction of total woody carbon mass across Washington and Oregon forests (64% of control plots remained on average). This study provides an applied example of a quasi-experimental approach to quantify the effects of a natural disturbance for which experimental settings are unavailable. The study results suggest that incorporating spatial information in addition to environmental covariates would yield a comparable set of control plots for wildfire effects quantification.

Environmental conditions promote local segregation, but functional distinctiveness allows aggregation of catfishes in the Amazonian estuary

Co-occurrence patterns of species can appear through niche-related processes such as (i) environmental filtering matching specific sets of traits to a given environment, and (ii) limiting similarity selecting divergent functional traits to reduce niche overlap. Locally, both processes should act together to shape the distribution of species. We evaluated the importance of environmental variables and functional distinctiveness to the co-occurrence patterns of nine marine catfishes in the inner estuary of the Amazon River mouth. Sampling was carried out in the dry seasons of 1996 and 1997, and the rainy season of 1996 by nearly 120 standardized bottom trawls per expedition. We observed 13 significant pairs of segregated species and two pairs of aggregated species, which sum 41.7% of all combinations. Amphiarius phrygiatus and Sciades couma segregated from all the remaining marine catfishes by occupying shallower areas with lower salinity levels. Aggregated pairs were strongly associated with higher functional distinctiveness. We concluded that environmental filtering is the main force structuring the co-occurrence patterns by promoting spatial segregation, but functional distinctiveness allowed some species to aggregate.

Seismic Ambient Noise Analyses Reveal Changing Temperature and Water Signals to 10s of Meters Depth in the Critical Zone

AbstractThe critical zone sustains terrestrial life, but we have few tools to explore it efficiently beyond the first few meters of the subsurface. Using analyses of high‐frequency ambient seismic noise from densely spaced seismometers deployed in the forested Shale Hills subcatchment of the Susquehanna Shale Hills Critical Zone Observatory (SSHCZO), we show that temporal changes in seismic velocities at depths from ∼1 m to tens of m can be detected. These changes are driven by variations at the land surface. The Moving‐Window Cross‐Spectral (MWCS) method was employed to measure seismic‐velocity changes in coda waves at hourly resolution in 10 different frequency bands. We observed a diurnal signal, a seasonal signal, and a meteorological‐event‐based signal. These signals were compared to time‐series measurements of precipitation, well water levels, soil moisture, soil temperature, air temperature, latent heat flux, and air pressure in the heavily instrumented catchment. Most of the velocity changes can be explained by variations in temperature that result in thermoelastic strains that propagate to depth. But some double minima in seismic velocity time‐series observed after large rain events were attributed in part to the effects of water infiltration. These results show that high‐frequency ambient noise data may in some locations be used to detect changes in the critical zone from ∼1 to ∼100 m or greater depth with hourly resolution. But interpretation of such data requires multiple environmental data sets to deconvolve the complex interrelationships among thermoelastic and hydrological effects in the subsurface critical zone.

The Effects of Globalization on Application of Environmental Management System (EMS)

Research background: Environmental management systems (EMS) is an international standard of best practices for managing impacts on the natural environment. The standard came into effect in 1996. The EMS reviews the environmental aspects and sets goals and targets for managing them. EMS is based on continuous development (Plan - Do - Check – Act). It is one of the voluntary instruments, as the obligation to introduce an EMS is not contained in any legal regulation. The mainstream of thoughts shows that globalization enforces EMS implementation mainly by sharing knowledge (best practices), but more aspects should be investigated. Purpose of the article: The article aims to formulate a conceptual model of globalization effects on the application of EMS and to document these effects on the selected company. Methods: A review of EMS literature is used to determine globalization’s positive and negative aspects of this field. To construct the model, we use the business process modelling tool Adonis CE. A case study of a selected company shows how these aspects affect the ability to adhere to its environmental policy. Findings & Value added: The article revealed a conceptual model of globalization effects on EMS. The presented case study showed the interconnection of individual effects and impact on the company’s effectiveness.

Characterization and toxicological evaluation of leachate from Bacolod city sanitary landfill using tilapia (Oreochromis niloticus): a preliminary study

This study was carried out to characterize and assess the toxicity of Bacolod City Landfill leachate using Tilapia as bio-indicator species. Acclimatized Oreochromis niloticus fishes (length of 14 cm ± 2.0 cm and weight of 11.8 g ± 0.9 g) were introduced into each treatment tank containing three different concentrations of leachate in duplicates (10 ppm, 50 ppm and 100 ppm) and a control group. Physical reactions were observed such as erratic swimming, loss of reflex, hyperactivities, and surfacing. Reactions and mortality rate increased with increasing concentration and duration of exposure. Mortality rate using One-way Analysis of Variance (ANOVA) and Duncan’s Multiple Range Test (DMRT) revealed a significant difference between treatment groups and exhibited 100 ppm as the most potent concentration. Heavy metal analysis of Cu and Pb as well as physicochemical analysis of color, Chimecal Oxygen Demande (COD) and Total dissolved Solids (TDS) exceeded the country’s environmental standard set by DENR. The 96 h bioassay stemmed the median lethal concentration (LC50) at 57.688 ppm. The study provides a biomarker database in taking action for the responsible enactment and enforcement of laws to upgrade, enhance and install framework for proper treatment of sanitary landfill leachate in cities and municipalities in the country.

Adaptation of Heuristic Evaluations for the Physical Environment

Previous work has investigated the need for domain specific heuristics. Nielsen’s ten heuristics offer a general list of principles, but those principles may not capture usability issues specific to a given interface. Studies have demonstrated methods to establish a domain specific heuristic set, but very little research has been conducted on interfaces in the physical environment, creating a gap in the state-of-the-art. The research described in this paper aims to address this gap by developing an environmental heuristic set; the heuristic set was developed specifically for the Houston light rail system, METRORail. Following development, the heuristic set was validated against Nielsen’s more general heuristics through several field studies. Results highlighted that there were significantly more usability issues identified when using the environment-based heuristics than the general heuristics. This suggests that domain specific heuristics provide a framework that allows evaluators to capture usability issues particular to the interface of the physical environment.

Bayesian analyses of an exponential-Poisson and related zero augmented type models

We consider several alternatives to the continuous exponential-Poisson distribution in order to accommodate the occurrence of zeros. Three of these are modifications of the exponential-Poisson model. One of these remains a fully continuous model. The other models we consider are all semi-continuous models, each with a discrete point mass at zero and a continuous density on the positive values. All of the models are applied to two environmental data sets concerning precipitation, and their Bayesian analyses using MCMC are discussed. This discussion covers convergence of the MCMC simulations and model selection procedures and considerations.

Multimer: Modeling Neurophysiological Experience in Public Urban Space

Measuring and analyzing spatial, multimodal biosensor data may effectively model how the built environment influences neurophysiological processes. This article presents the Multimer Data Collection and Analysis System (MDCAS), which records data from several kinds of commonly available, wearable sensors (wearables) including electroencephalogram (EEG), electrocardiogram (ECG), pedometer, accelerometer, and gyroscope modules. Data from these wearables is sent to a custom smartphone application, which also records surveys and associates these with global positioning system (GPS) readings. MDCAS then collects and analyzes data from its smartphone app. MDCAS aims to help space professionals like architects, workplace strategists, and urban planners make better design interventions. As a case study of the MDCAS, this article discusses the analysis results of biometric data (EEG, ECG in addition to survey reports) collected from a 2017 study focused on pedestrians, cyclists, and drivers (N = 101) in New York City. Signal and spatial validation of the data indicated usability—data that is not randomly distributed—for biometric data types. Exploratory regressions of the biometric data (regressors) with exogenous data (predictors including environmental and municipal data sets) revealed spatiotemporal relationships that warrant further investigation. Notable relationships include 1) EEG beta and gamma frequencies were more strongly predicted by street features like service capacity (e.g. delivery levels) and speed limit, while EEG delta and theta frequencies were more strongly predicted by amenities like cultural institutions and trees; 2) pedestrians and cyclists were more impacted by street features during weekdays, and 3) a non-oppositional relationship between EEG beta/gamma and delta/theta frequencies.

Random forest classification to determine environmental drivers and forecast paralytic shellfish toxins in Southeast Alaska with high temporal resolution

Paralytic shellfish poison toxins (PSTs) produced by the dinoflagellate in the genus Alexandrium are a threat to human health and subsistence lifestyles in Southeast Alaska. It is important to understand the drivers of Alexandrium blooms to inform shellfish management and aquaculture, as well as to predict trends of PST in a changing climate. In this study, we aggregate environmental data sets from multiple agencies and tribal partners to model and predict concentrations of PSTs in Southeast Alaska from 2016 to 2019. We used daily PST concentrations interpolated from regularly sampled blue mussels (Mytilus trossulus) analyzed for total PSTs using a receptor binding assay. We then created random forest models to classify shellfish above and below a threshold of toxicity (80 µg 100 g−1) and used two methods to determine variable importance. We obtained a multivariate model with key variables being sea surface temperature, salinity, freshwater discharge, and air temperature. We then used a similar model trained using lagged environmental variables to hindcast out-of-sample (OOS) shellfish toxicities during April-October in 2017, 2018, and 2019. Hindcast OOS accuracies were low (37–50%); however, we found forecasting using environmental variables may be useful in predicting the timing of early summer blooms. This study reinforces the efficacy of machine learning to determine important drivers of harmful algal blooms, although more complex models incorporating other parameters such as toxicokinetics are likely needed for accurate regional forecasts.

Quantifying effects of tracking data bias on species distribution models

Abstract Telemetry datasets are becoming increasingly large and covering a wider range of species using different technologies (GPS, Argos, light‐based geolocation). Together, such datasets hold tremendous potential to understand species' space use at broad spatial scale, through the development of species distribution or habitat suitability models (SDMs) to predict environmental dependencies of species across space and time. However, tracking datasets can be heavily biased and an assessment of how such biases affect SDM predictions, and therefore, our interpretation of animal distributions is lacking. We generated simulated tracks based on predetermined environmental values for a random predator and a central place forager, and then sampled positions from those tracks based on a combination of five common biases in tracking datasets: (a) tagging location; (b) tracking device; (c) data gaps within tracks; (d) premature tag detachment (or failure) and (e) different processing methods. We then used 240 combinations of the resulting biased simulated datasets to develop binomial generalised linear (GLM) and additive (GAM) models to estimate habitat suitability in different environmental sets (cool deep, cool coastal, warm deep and warm coastal environments). Our results show that tagging location and length of tracks have the largest effects in decreasing model performance, but that these biases can be overcome by adding a small percentage of additional, relatively less biased tracks to the dataset. In comparison, the effects from all other biases were almost negligible, including for low resolution tracking datasets for which sufficient tracks are available. We also highlight the need for a cautionary approach when using processing methods that can introduce other biases (e.g. interpolated locations). Similar trends were obtained for the random predator and the central place forager, but with relatively lower model performance for the latter. We provide evidence that even non‐GPS tracking datasets can be readily used to improve the knowledge of large‐scale space use by species without the need for detailed processing and tracking reconstruction. This is especially relevant in the current context of rapid increase in data acquisition and the urgent need to address the large spatial scale ecological consequences of global change.

Hybrid control for tracking environmental level sets by nonholonomic robots in maze-like environments

A non-holonomic constant-speed robot travels in an unknown maze-like environment cluttered with complex obstacles. Through the obstacle-free part of the plane, the robot should autonomously arrive at the isoline where an unknown scalar field assumes a given value. Afterwards, it should track the obstacle-free part of the isoline. The robot has access only to the field value at the current location and the distance from this location to the obstacles. We present a hybrid nonlinear navigation law that solves this mission. The law does not use estimation of the field gradient and is non-demanding with respect to both computation and motion. The non-local convergence of the proposed algorithm is rigorously justified and confirmed by computer simulation tests.

Residential Greenspace in Childhood Reduces Risk of Pediatric Inflammatory Bowel Disease: A Population-Based Cohort Study.

Environmental factors related to urbanization and industrialization are believed to be involved in inflammatory bowel disease (IBD) development, but no study has looked at the association between greenspace and IBD. We conducted a retrospective cohort study using linked population-based health administrative and environmental data sets. The study population comprised 2,715,318 mother-infant pairs from hospital births in Ontario, Canada, between April 1, 1991, and March 31, 2014. We measured the exposure to residential greenspace using the normalized difference vegetation index derived using remote-sensing methods. Average greenspace was estimated for the pregnancy and childhood periods. We used mixed-effects Cox proportional hazard models to assess potential associations between residential greenspace and the risk of developing IBD before 18 years while adjusting for covariates including sex, maternal IBD, rural/urban residence at birth, and neighborhood income. There were 3,444 IBD diagnoses that occurred during follow-up. An increase in the interquartile range of residential greenspace during the childhood period was associated with a lower risk of developing pediatric-onset IBD (hazard ratio [HR] 0.77, 95% confidence interval [CI] 0.74-0.81). This relationship was significant for both ulcerative colitis (HR 0.72 95% CI 0.67-0.78) and Crohn's disease (HR 0.81, 95% CI 0.76-0.87). There was a linear dose response across increasing quartiles of greenspace (P < 0.0001). No consistent association was detected between maternal intrapartum greenspace exposure and pediatric-onset IBD. Higher exposure to residential greenspace during childhood was associated with a reduced risk of IBD, suggesting a novel avenue to prevent IBD in children.

Migration and penetration of Meloidogyne enterolobii and M. incognita in soil columns with tomato and marigold

Root-knot nematodes (Meloidogyne spp.) can parasitize and reduce yields of nearly all agricultural crops. Although migration studies focus on the model species, M. incognita, there is little information about M. enterolobii, which has been a major threat for guava in Brazil. The vertical migration of second-stage juveniles (J2) of M. enterolobii and M. incognita was evaluated to determine the effect of good host and nonhost plants on nematode motility. Fourteen-cm long soil columns were constructed of PCV rings with Styrofoam cups containing a 4-week-old tomato or marigold (Tagetes patula) seedlings as plant stimuli; host-free cups were used as nontreated control. After inoculating 1000 freshly-hatched J2, the columns were placed in environmental chamber sets at 20 °C. The columns were disassembled at 3, 6, and 9 days after inoculation (DAI). The experiment was completely randomized block with four replicates. J2 were extracted from each ring and cup, while tomato and marigold roots were stained to observe J2 that penetrated roots. Data were subjected to repeated-measure MANOVA and chi-square test to compare J2 distribution along the columns. The migrations under tomato and marigold stimuli were comparable to those under host-free columns. Although both species migrated over 13 cm, M. enterolobii was more mobile than M. incognita. Juveniles of M. enterolobii infected tomato roots as early as 3 DAI, whereas marigold delayed nematode penetration. Our results show that two species of root-knot nematodes behave differently within the soil, and under the same conditions J2 of M. enterolobii migrate faster than M. incognita.

Sound event aware environmental sound segmentation with Mask U-Net

This paper proposes an environmental sound segmentation method using Mask U-Net. In recent years, human–robot interactions, especially speech dialogue, have been assessed by auditory scene analysis. Methods, such as noise reduction, section detection, and sound source separation have been proposed for robot audition, acoustic signal processing, and machine learning. However, such conventional approaches have three drawbacks: (1) Many studies have analyzed individual functions, which are regarded as being a cascade. Cascade systems can, however, result in the accumulation of errors generated at each functional block. (2) Unlike conventional cascade systems, deep learning-based methods that simultaneously detect sections, separate sound sources, and identify classes have also been proposed for speech separation. These techniques can be extended for multiple classes of environmental sounds, but their performance becomes degraded with large variations in sound event lengths among classes. (3) In addition, these methods have recurrent neural network layers making it difficult to process calculations in parallel. This paper proposes an environmental sound segmentation method called Mask U-Net, which robustly differentiates sound event lengths among classes. Simulation experiments using a developed 75-class environmental sound data set showed that the proposed method was faster than conventional methods and showed high segmentation performance.

Fluoride contamination in and around selected geothermal sites in Odisha, Eastern India: assessment of ionic relations, fluoride exposure and remediation.

Fluoride contamination in groundwater is a major problem throughout the world as well as in India. High-fluoride content was reported in the hot springs of Atri and Tarbalo sites in Odisha, India, and residents of nearby villages showed the manifestations of fluorosis. Around 39% of the groundwater samples showed fluoride concentration > 1 mg/l, higher than the desirable limit specified by the WHO. The dominant chemical facies of groundwaters were ions of Ca-Mg-HCO3 and Ca-Na-Cl, which infers the lithological control over the hydrochemistry of this area. A strong correlation between fluoride and other major ions could not be found, suggesting that multiple processes are responsible for the enriched fluoride concentration observed in the study area. The major geochemical processes include dissolution of fluoride-bearing minerals from the rocks, evapotranspiration, agricultural input and mixing of cold groundwater with hot spring water containing high fluoride. The maximum fluoride exposure doses through drinking water from fluoride-contaminated tube wells were estimated to be 0.07 mg/kg/d for infants, 0.125 mg/kg/d for children and 0.06 mg/kg/d for adults, which are higher than the minimum risk level (0.05 mg/kg/d). Exposure doses of fluoride indicate that exposure risk is doubled for children in comparison to infants and adults, which might cause severe dental fluorosis and other ailments. Considering the environmental and hydrological set up of the study area, membrane defluoridation process can be suggested as the best remediation method. Nalgonda technique, dilution of fluoride-rich groundwater and better nutrition containing calcium and vitamin C are other possible options that can be included for early mitigation of fluoride contamination.

The Urban Built Environment, Walking and Mental Health Outcomes Among Older Adults: A Pilot Study

The benefits of walking in older age include improved cognitive health (e.g., mental alertness, improved memory functioning) and a reduced risk of stress, depression and dementia. However, research capturing the benefits of walking among older people in real-time as they navigate their world is currently very limited. This study explores cognitive health and well-being outcomes in older people as they walk in their local neighborhood environment. Residents from an independent living facility for older people (mean age 65, n = 11) walked from their home in two dichotomous settings, selected on the basis of significantly different infrastructure, varying levels of noise, traffic and percentage of green space. Employing a repeated-measures, cross over design, participants were randomly allocated to one of two groups, and walked on different days in an urban busy “gray” district (a busy, built up commercial street) vs. an urban quiet “green” district (a quiet residential area with front gardens and street trees). Our study captured real-time air quality and noise data using hand-held Airbeam sensors and physiologic health data using a smart watch to capture heart rate variability (a biomarker of stress). Cognitive health outcome measures were a pre- and post-walk short cognitive reaction time (SRT) test and memory recall of the route walked (captured via a drawn mental map). Emotional well-being outcomes were a pre- and post-walk mood scale capturing perceived stress, happiness and arousal levels. Findings showed significant positive health benefits from walking in the urban green district on emotional well-being (happiness levels) and stress physiology (p < 0.05), accompanied by faster cognitive reaction times post-walk, albeit not statistically significant in this small sample. Cognitive recall of the route varied between urban gray and urban green conditions, as participants were more likely to rely on natural features to define their routes when present. The environmental and physiologic data sets were converged to show a significant effect of ambient noise and urban conditions on stress activation as measured by heart rate variability. Findings are discussed in relation to the complexity of combining real-time environmental and physiologic data and the implications for follow-on studies. Overall, our study demonstrates the viability of using older people as citizen scientists in the capture of environmental and physiologic stress data and establishes a new protocol for exploring relationships between the built environment and cognitive health in older people.

Active learning for anomaly detection in environmental data

Due to the growing amount of data from in-situ sensors in environmental monitoring, it becomes necessary to automatically detect anomalous data points. Nowadays, this is mainly performed using supervised machine learning models, which need a fully labelled data set for their training process. However, the process of labelling data is typically cumbersome and, as a result, a hindrance to the adoption of machine learning methods for automated anomaly detection. In this work, we propose to address this challenge by means of active learning. This method consists of querying the domain expert for the labels of only a selected subset of the full data set. We show that this reduces the time and costs associated to labelling while delivering the same or similar anomaly detection performances. Finally, we also show that machine learning models providing a nonlinear classification boundary are to be recommended for anomaly detection in complex environmental data sets.

The AirSensor open-source R-package and DataViewer web application for interpreting community data collected by low-cost sensor networks

While large-scale low-cost sensor networks are now recording air pollutant concentrations at finer spatial and temporal scales than previously measured, the large environmental data sets generated by these sensor networks can become overwhelming when considering the scientific skills required to analyze the data and generate interpretable results. This paper summarizes the development of an open-source R package (AirSensor) and interactive web application (DataViewer) designed to address the environmental data science challenges of visualizing and understanding local air quality conditions with community networks of low-cost air quality sensors. AirSensor allows users to access historical data, add spatial metadata, and create maps and plots for viewing community monitoring data. The DataViewer application was developed to incorporate the functionality and plotting functions of the R package into a user-friendly web experience that would serve as the primary source for data communication for community-based organizations and citizen scientists.