Environmental Model Research Articles

How does a dynamic surface roughness affect snowpack modeling?

The SNOWPACK model is a cryosphere model which incorporates several environmental model parameters, one of which being the aerodynamic roughness length (z0). The z0 is considered a static parameter, however, research has shown that the z0 of the surface is variable due to the changing nature of the snowpack surface throughout the winter season. This study highlights the sensitivity of the z0 within the SNOWPACK model based on the outputs of sublimation, SWE, and sensible heat. The z0 values were calculated in two ways, anemometrically (z0-A), using a wind profile, and geometrically (z0-G), measuring surface geometry. Calculated z0-A values were between 1.03 × 10−6 to 0.12 m. The z0-G values were calculated from a terrestrial lidar scan using various resolution values of post-process resolutions. These resolutions of 0.01, 0.1, and 1 m resulted in z0-G values of 0.26, 0.08, and 0.01 m, respectively. Therefore, as the resolution coarsened, the z0-G values decreased. Lastly, these calculated z0-G values, a variable run, using weekly measured z0-G values, and 0.002 (SNOWPACK default), 0.02, and 0.2 m values were incorporated into the SNOWPACK model. When applied, cumulative sublimation, SWE, and sensible heat outputs varied by 131%, −71%, and −49%, when compared to the default z0 value used within the model.

From planetary scenarios to planetary sensing: Models, observations, and political legibility

This paper explores the political uses of images generated by Earth System science. It argues that images of possible climate futures, maps of potential worlds of heatwaves and wildfires, are made legible to policymakers by an alliance with a class of climate-economy models that associate scientific estimates of climate impacts with a prescribed international policy and technology mix. While environmental models have successfully mobilized policymakers in the past by providing images of “planetary scenarios” accompanying different emissions pathways, with climate change a political actor outside the administrative state is required to overcome the entrenchment of fossil capital. The paper suggests such actors are empowered not by the rhetoric of scenario modeling but by the emerging practice of “planetary sensing,” where activists and stakeholders directly mobilize the planetary images generated by Earth System science as they work to evacuate prisons, track pollutants, and repair pipelines.

Implementation of the Environmental Exploration Learning Model (JAS) Assisted by Google Lens on the Scientific Literacy of Taman Mulia High School Students

This study aims to determine: (1) the difference in science literacy of class X students on plantae material with the Explore Nature Around (JAS) model assisted by google lens, (2) the effect of applying the Explore Nature Around (JAS) learning model assisted by google lens on students' science literacy on plantae material class X Taman Mulia. This type of research is Quasi experiment with Nonequivalent Control Group Design. The population of this study were students of class X Taman Mulia. The sample consisted of two classes determined by random sampling technique. The instruments used consisted of science literacy tests and learning process observation sheets. The data were analyzed by t-test, the analysis was carried out using SPSS version 29.01 for windows which previously conducted homogeneity and normality tests at a significant level of 0.05. Based on the results of the analysis, the conclusions were obtained. (1) The difference in science literacy of class X students on plantae material with the Explore Nature Around (JAS) model assisted by google lens (2) the effect of applying the Explore Nature Around (JAS) learning model assisted by google lens on students' science literacy in plantae material class X Taman Mulia.

Sensitivity assessment of optimal control strategies and cost-effectiveness analysis of a novel Candida Auris environmental transmission model in intensive care facilities

Candida Auris is an emerging fungal pathogen flagged by CDC as a serious global health threat among nosocomial infections in the recent times. As an evolving pathogen that often goes misidentified or unidentified under standard laboratory tests, it has the ability to cause fatal infections among the target population involving patients with serious medical conditions admitted to intensive care facilities, due to its capacity to resist anti-fungal treatment and the ability to persist in the hospital environment for long periods. The subject of this paper is to develop a deterministic model to study the transmission nature of Candida Auris wherein measures like apt admission screening methods with weekly screening follow-ups, transmission prevention, proper treatment protocols and environmental disinfection procedures are introduced as constant mitigating controls into the model initially which are later redefined as variable control functions during the optimal control analysis. The theory of optimal control implemented into the model helps us to understand the sensitivity of each control strategy upon the behaviour of each state variable. Further, cost-effectiveness analysis is rigorously conducted using incremental cost-effectiveness ratio (ICER) to identify and rank the control strategies involved based on their economic efficiency. Numerical simulation for the optimal control analysis is performed in MATLAB using the Forward–Backward Sweep Method and the findings are illustrated graphically.

Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization. The integration of artificial intelligence (AI) in these assessments has shown great promise, yet the "black box" nature of AI models often undermines trust due to the lack of transparency in their decision-making processes, even when these models demonstrate high accuracy. To address this challenge, we evaluated the performance of a transformer model against other AI approaches, utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators. We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments, enabling the identification of individual indicators' contributions to the model's predictions. We find that the transformer model outperforms others, achieving an accuracy of about 98% and an area under the receiver operating characteristic curve (AUC) of 0.891. Regionally, the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas, level IV in the northern region, and level V in the western region. Through explainability analysis, we identify that water hardness, total dissolved solids, and arsenic concentrations are the most influential indicators in the model. Our AI-driven environmental assessment model is accurate and explainable, offering actionable insights for targeted environmental management. Furthermore, this study advances the application of AI in environmental science by presenting a robust, explainable model that bridges the gap between machine learning and environmental governance, enhancing both understanding and trust in AI-assisted environmental assessments.

Performance analysis and multi-objective optimization of a novel solid oxide fuel cell-based poly-generation and condensation dehumidification system

The application of energy supply systems based on fuel cells contributes to meeting the requirement of sustainable development, and developing high-efficiency, low-pollution, and multifunction poly-generation systems coupled with fuel cells is increasingly attractive. This study proposes a novel combined cooling, heating, power poly-generation and dehumidification system including a solid oxide fuel cell, a gas turbine, a double effect absorption refrigerator, a condensation dehumidification unit, an organic Rankine cycle, and a heat exchanger. Thermodynamic, economic, and environmental analysis models of the entire system are developed to evaluate system comprehensive performance. The effects of system parameters such as the inlet temperature of fuel cell, steam to carbon ratio, air flow rate, and high temperature generator temperature on system performance are analyzed. The analysis findings reveal that the proposed system can provide power, cooling, and heating of 551.904 kW, 20.307 kW, and 193.009 kW, and the dehumidification capacity per unit is 14.516 g/kg when 1.238 kg/s of humid air is treated. The system exergy efficiency is found to be 70.534 %, and the system cost rate is 15.578 $/h with carbon dioxide emission being 0.301 kg/kWh at the design condition. Two groups of multi-objective optimizations of the whole system are further conducted to acquire the optimal system performance and working conditions. The optimization results demonstrate that the optimal exergy efficiency of 72.912 % appears at point C1 in the first group of optimization with the lowest carbon dioxide emission being 0.293 kg/kWh, and the optimal qualities of humid air are found to be 1.606 kg/s at C2 and D2 in the second group of optimization. After optimization, the system carbon dioxide emission is decreased by 0.008 kg/kWh, the exergy efficiency is increased by 2.378 %, and the quality of humid air in dehumidification process is developed by 0.368 kg/s. In summary, the proposed poly-generation and dehumidification system can meet multifunction energy demands and achieve good performance, and the research work could also provide theoretical basis for developing evaluation and optimization technologies of fuel cell-based poly-generation systems.

Assessing reproductive effects and epigenetic responses in Austrolebias charrua exposed to Roundup Transorb®: Insights from miRNA profiling and molecular interaction analysis

This study examines the effects of Roundup Transorb® (RDT) exposure on reproductive functions and ovarian miRNA expression in Austrolebias charrua. Exposure to RDT (at 0.065 or 5 mg. L−1 for 96 h) significantly disrupts fertility, evidenced by changes in fertilization rates and egg diameter. Profiling of ovarian miRNAs identified a total 205 miRNAs in A. charrua. Among these, three miRNAs were upregulated (miR-10b-5p, miR-132–3p, miR-100–5p), while ten miRNAs were downregulated (miR-499–5p, miR-375, miR-205–5p, miR-206–3p, miR-203a-3p, miR-133b-3p, miR-203b-5p, miR-184, miR-133a-3p, miR-2188–5p) compared to non-exposed fish. This study reveals that differentially expressed miRNAs are linked to molecular pathways such as steroid hormone biosynthesis, lipid and carbohydrate metabolism, bioenergetics, and antioxidant defense. It also analyzes molecular interactions between miRNAs and target genes during RDT exposure in annual killifish, providing insights into biomarkers in ecotoxicology. Moreover, it provides scope for developing environmental health assessment models based on epigenomic endpoints, supporting the protection of biodiversity and ecosystem services through the quantification of stress responses in living organisms exposed to pesticides.

Refining Environmental Sustainability Governance Reports through Fuzzy Systems Evaluation and Scoring

Environmental, Social, and Governance (ESG) reports have become essential tools for enterprises to showcase their commitment to sustainable development and social responsibility. However, discrepancies persist regarding the criteria, assessments, and ratings disclosed in these reports. Moreover, there is a need for more objective methods to determine the weight distribution of indicator items. This study introduces a novel approach utilizing semantic variables in fuzzy theory and a multiple logic fuzzy inference system to develop an ESG environmental management performance assessment model. Therefore, this paper aims to develop a novel approach utilizing semantic variables and a multiple logic fuzzy inference system to quantitatively evaluate the sustainable performance of an environmental management plan. This research also aims to ensure fair and objective assessment outcomes, providing valuable guidance for enterprises in implementing performance management strategies. Key aspects investigated include the impact of membership functions, the extended utilization of semantic variables and logical rules, a comparative analysis of traditional weight assessments, and the limitations of applying fuzzy theory. Through comprehensive discussions and calculations, it is evident that fuzzy theory offers considerable flexibility in application. By tailoring fuzzy rules and selecting appropriate membership functions, diverse application scenarios can be accommodated. The Fuzzy systems evaluation and scoring EMP model generates EMP evaluation scores ranging from 1.76 to 8.29 for Gaussian membership, 1.80 to 8.19 for Triangular membership-A, 1.92 to 8.00 for Triangular membership-B, and 1.81 to 8.19 for Quadrilateral trapezoidal membership, based on simulated rating scenarios using the semantic variables of completeness and feasibility. This approach successfully incorporates distribution logic from subjective membership degrees to evaluate EMP scores. The findings demonstrate that fuzzy theory enables the consideration of multiple factors and facilitates the provision of objective-level membership, underscoring its potential in addressing complex evaluation challenges. This study illuminates the versatility of the fuzzy system theory, with its applications poised to extend across various domains.

Comparative analysis of eight urea-electricity-heat-cooling multi-generation systems: Energy, exergy, economic, and environmental perspectives

The Haber-Bosch process for urea production is known for its significant carbon emissions. While the chemical looping ammonia generation method presents a potential alternative, its technical and economic feasibility still requires further investigation. This paper conducts a comparative analysis of eight renewable energy-driven urea-electricity-heat-cooling multi-generation systems by developing their energy, exergy, economic, and environmental evaluation models. Considering the fluctuation in renewable energy resources, an annual evaluation of the different systems is conducted based on a monthly time scale. The results indicate that systems based on the Haber-Bosch process have higher average energy and exergy efficiencies of 60–80 % and 40–50 %, respectively. For the chemical looping ammonia generation process, the respective efficiencies are 50–70 % and 30–40 %. Additionally, the system integrating the Haber-Bosch reaction, reduction reactor, and solid oxide fuel cell yields the highest energy and exergy efficiencies among the eight systems, at 68.2 % and 45.78 %, respectively. However, it is inferior to the multi-generation system integrating the chemical looping ammonia generation process in terms of economic and environmental performance. In addition, the system integrating the chemical looping ammonia generation process and solid oxide fuel cell has the shortest discounted payback period of 8 years, with annual revenue and net present value of 32.63 and 11.66 MUSD, respectively. While the operating expenditures of the Haber-Bosch (12.6–15.7 MUSD) are lower than those of the chemical looping ammonia generation process (14.0–17.5 MUSD), its capital expenditures (382.0–410.8 MUSD) are significantly higher than those of the chemical looping ammonia generation process (302.4–307.3 MUSD). Furthermore, the conducted environmental evaluation shows that the multi-generation system integrating the chemical looping ammonia generation process and reduction reactor exhibits lower overall carbon emissions (0.013 t CO2/t urea) compared to the state-of-the-art method of oxy-fuel coal-fired power plant for urea synthesis.

Phylogeny, niche modelling and morphology unveil a new lianescent species of Sigmoidotropis (Leguminosae, Papilionoideae) across rainforest enclaves within Brazilian dry vegetation

AbstractThe small Neotropical papilionoid legume genus Sigmoidotropis (nine species) has been described only relatively recently as the result of the taxonomic rearrangement of the genus Vigna (subtribe Phaseolinae), based on phylogenetic analyses of DNA sequence data. Here, we formally propose a new lianescent species of Sigmoidotropis that occurs disjunctly in enclaves of moist forest of mountainous regions surrounded by dry vegetation at three different localities in the Brazilian states of Bahia and Minas Gerais. Its phylogenetic position and morphological affinities with other genera of Phaseolinae were evaluated by Bayesian inference and maximum likelihood analyses, using sequence data from the nuclear ITS/5.8S and plastid trnK/matK. To better understand this new species’ widely disjunct distribution and a possible connection between its populations in the past, an environmental niche modelling (ENM) analysis was performed. The phylogenetic analyses revealed that Sigmoidotropis appears closest to the genera Ancistrotropis and Delgadoana, together forming the Sigmoid‐keel clade. The new species proposed here is strongly recovered within Sigmoidotropis, being most closely related to S. ekmaniana, S. elegans, and S. megatyla. The ENM indicated changes in the potential distribution of the new species over different climatic periods, with expansion during colder periods and contraction during warmer periods, leading to possible relictual populations in moist forest enclaves. A description of the new species Sigmoidotropis maculata is presented, along with illustrations, a distribution map, taxonomic comments, and conservation status assessment.

Comprehensive performance analysis and optimization for PEMFC-heat pump combined system considering dual heat source

Traditional proton exchange membrane fuel cell (PEMFC) systems face the challenges of strong thermal–electrical coupling and insufficient heating capacity. This study proposes a PEMFC–heat pump combined system considering dual heat source, which effectively utilizes fuel cell waste heat and environmental heat to achieve significant improvements in thermal–electrical decoupling and heating capacity. Energy, economic, and environmental analysis models were established, and the effects of key parameters on the combined system were systematically analyzed, followed by multi-objective optimization. Results show that the PEMFC current density has the largest effect on the heating capacity, while the heat flow ratio exerts the strongest influence on economic performance. The minimum pollutant emissions are achieved when wind power is adopted for hydrogen production. Under an ambient temperature of 248.15 K, the combined system increases the heating capacity by 12.8 % and reduces the levelized cost of energy and greenhouse gas emissions by 7.85 % and 15.9 %, respectively. The optimal solution presents a strong nonlinear relationship with the PEMFC current density and condenser undercooling, the heat flow ratio is close to the upper limit (0.99), and the operating temperature is close to the lower limit (333.4 K). These findings provide innovative thermoelectric decoupling and efficient energy supply solutions for PEMFC systems and have important implications for accelerating the low-carbon transition of regional energy systems.

Aggregate risk assessment for multi-route exposure to hazardous chemicals caused by chemical accidents, with a focus on toluene

Chemical accidents significantly impact environmental and human health. However, studies investigating the impacts of such accidents have primarily focused on single-route exposures, potentially underestimating the extent of damage. This study aimed to conduct an aggregate risk assessment for multi-route exposure to hazardous chemicals to ensure systematic and rational management of the health impacts on residents exposed to chemical accidents, considering the behavior of a hazardous chemical from a chemical accident within environmental media. Drawing upon a real chemical accident that occurred in Siheung, Gyeonggi-do, in 2019, leakage of 500 L of toluene over an hour was assumed. Employing a multimedia environmental dynamics model, the time-dependent concentrations across various environmental media were calculated, and the average daily dose (ADD), hazard quotient (HQ), and hazard index (HI) for each exposure route included in the multi-route exposure assessment were derived. Health risks were deemed present if the calculated HQ and HI values exceeded the threshold of 1. The results indicated the highest ADD values among the 0–9 age group, with inhalation exposure registering the highest ADD across all exposure routes. However, no significant health risks were observed, with both HQ and HI values not exceeding 1. This aggregate risk assessment approach is proposed as an effective preliminary evaluation method for health impact assessments in areas affected by chemical accidents.Graphical

Meeting Increasing Demands for Upstream Scope 3 Reporting: A Framework for the Drilling Industry

Summary This paper details the scope and framework applied for quantifying upstream Scope 3 greenhouse gas (GHG) emissions of offshore drilling operations. A step-by-step methodological approach is presented, comprising environmental impact modeling of drilling and rig operational activities through the application of the life cycle assessment (LCA) methodology. The study aims to break down upstream Scope 3 emissions to a process level, demonstrating how upstream Scope 3 emissions can be quantified and evaluated to a greater detail level compared with what current Scope 3 reporting levels offer today. A North Sea drilling case study conducted in collaboration with a major drilling contractor is used for showcasing the impact burden levels, challenges, and prosperity of accounting, reporting, and tracking upstream Scope 3 emissions as a drilling contractor. A total of 141 drilling and rig operational activities were identified and prioritized for impact calculation, presenting a detailed breakdown of upstream Scope 3 emissions results for offshore well drilling. Impacts are quantified in tonnes of carbon dioxide equivalent (CO2eq.) emitted per day in operation. The study does not include downstream Scope 3 and reports limited to direct (Scope 1) emissions. Results show a total upstream Scope 3 emissions burden of 412 tCO2eq./d in operation. Of the eight assessed upstream Scope 3 categories, purchased goods account for the largest contribution of 32%. Activities related to the operation and maintenance of drilling equipment account for the largest impact contribution of 351 tCO2eq./d, corresponding to 86% of total upstream Scope 3 emissions. The reported outputs may serve as a benchmark for future Scope 3 accounting and reporting within the drilling industry, as well as for LCA inventories for oil and gas production. A set of system boundaries for assessing upstream Scope 3 emissions is suggested, along with recommended emission calculation methods. The presented framework may serve as an alternate supplement to the International Petroleum Industry Environmental Conservation Association (IPIECA) adapted version of the GHG Protocol’s Scope 3 Standard in terms of Scope 3 inventory boundary setting and data prioritization with relevance to drilling contractors, and finally to the International Association of Drilling Contractors (IADC) industry guideline for environmental, social, and governance (ESG) reporting.

Calibration guide for watershed modeling with distributed groundwater modeling: application for the SWAT+ model

ABSTRACT We present a new calibration strategy guide for coupled surface–subsurface hydrologic models. The goal is to reduce bias in environmental modeling and make calibration processes more consistent. The strategy guide enhances model efficiency and accuracy by focusing on changes to channel, soil, landscape, and aquifer properties. This approach is particularly beneficial when simulating important hydrologic features like baseflow and peaks and across different watersheds. Developed using the Soil and Water Assessment Tool (SWAT+) and the new gwflow module in six diverse US watersheds, each with unique hydrologic characteristics, the guide encourages improved calibration through routine use. It addresses specific issues related to the temporal distribution of streamflow and offers a robust method for enhancing model performance across a wide range of hydrologic conditions. By employing parameter estimation and sensitivity analysis, the approach ensures rigorous and objective calibration of SWAT+ and other models that include land, soil, aquifer, and water processes, leading to more accurate hydrologic modeling.

Shadow banking and climate change, the “hidden leaf” of green credit risk policy

The paper studies the influence of shadow banking on the efficiency of two important green prudential proposals for credit risk of traditional banks : the Green Supporting Factor (GSF) and the Brown Penalizing Factor (BPF). Through an environmental general equilibrium model, the paper shows that, without shadow banks in the model, the use of the BPF or GSF generates a negative relationship between banking stability and environmental benefits. By introducing shadow banks, the use of BPF allows to maintain banking stability and to generate environmental benefits at the same time. These results emphasize the need to take into account shadow banking sector in a consistent assessment of green credit risk regulation proposals.

Different effects of carbon pricing and border adjustment in Germany and Spain

This study examines the effects of carbon pricing in Germany, Spain, and the broader European context using the dynamic, three-region environmental multisector general equilibrium model, EMuSe. Our findings indicate that unilateral carbon pricing in Germany or Spain leads to a sustained negative output. The marginal increase in production costs outweighs modest reductions in emissions. However, when Europe collectively adopts carbon pricing, the long-term output effects become positive, although there are more significant transition costs due to close trade relations within Europe. We find evidence of carbon leakage, which is marginally mitigated by a border adjustment mechanism. However, this mechanism mainly protects domestic carbon-intensive sectors and produces inconsistent country outcomes; Germany gains, while Spain loses. Notably, Spain’s energy sector emerges as a long-term beneficiary due to its relatively lower emission intensity. The findings highlight the strategic importance for Europe to engage global partners in carbon pricing as it reduces the economic downturn phase and increases long-term gains.

Cross-country analysis of the effectiveness of commercialization of scientific research results

The objective of this research is to undertake a comparative analysis of methodologies for evaluating the efficacy of research commercialization across diverse nations, focusing particularly on the United States, Australia, Canada, South Korea, the European Union, and Kazakhstan. The study methodology involved a comparative analysis, commencing with an exhaustive examination of academic and practical resources to pinpoint key organizations involved in research commercialization within the specified countries. Subsequently, after the selection of countries and a systematic scrutiny of these organizations' activities, the methodology endorsed by the Alliance of Technology Transfer Professionals (ATTP) was employed to compare and assess commercialization efficiency across varied nations. Results of the study: the study outcomes unveiled common trends and effective assessment approaches, while also identifying deficiencies in Kazakhstan's commercialization evaluation system and offering recommendations for enhancement. The assessment of commercialization metrics in Kazakhstan is hindered by the paucity of comprehensive data, rendering comparisons with global benchmarks, including the ATTP methodology, challenging. However, concerted collaboration among governmental bodies, research institutions, and industrial stakeholders could surmount these hurdles and foster innovative entrepreneurship within the nation. The adoption of alternative research methodologies such as environmental functioning analysis models or regression analysis may enable a more profound evaluation of commercialization efficiency in Kazakhstan, notwithstanding data constraints.

Random forests with spatial proxies for environmental modelling: opportunities and pitfalls

Abstract. Spatial proxies, such as coordinates and distance fields, are often added as predictors in random forest (RF) models without any modifications being made to the algorithm to account for residual autocorrelation and improve predictions. However, their suitability under different predictive conditions encountered in environmental applications has not yet been assessed. We investigate (1) the suitability of spatial proxies depending on the modelling objective (interpolation vs. extrapolation), the strength of the residual spatial autocorrelation, and the sampling pattern; (2) which validation methods can be used as a model selection tool to empirically assess the suitability of spatial proxies; and (3) the effect of using spatial proxies in real-world environmental applications. We designed a simulation study to assess the suitability of RF regression models using three different types of spatial proxies: coordinates, Euclidean distance fields (EDFs), and random forest spatial prediction (RFsp). We also tested the ability of probability sampling test points, random k-fold cross-validation (CV), and k-fold nearest neighbour distance matching (kNNDM) CV to reflect the true prediction performance and correctly rank models. As real-world case studies, we modelled annual average air temperature and fine particulate air pollution for continental Spain. In the simulation study, we found that RFs with spatial proxies were poorly suited for spatial extrapolation to new areas due to significant feature extrapolation. For spatial interpolation, proxies were beneficial when both strong residual autocorrelation and regularly or randomly distributed training samples were present. In all other cases, proxies were neutral or counterproductive. Random k-fold cross-validation generally favoured models with spatial proxies even when it was not appropriate, whereas probability test samples and kNNDM CV correctly ranked models. In the case studies, air temperature stations were well spread within the prediction area, and measurements exhibited strong spatial autocorrelation, leading to an effective use of spatial proxies. Air pollution stations were clustered and autocorrelation was weaker and thus spatial proxies were not beneficial. As the benefits of spatial proxies are not universal, we recommend using spatial exploratory and validation analyses to determine their suitability, as well as considering alternative inherently spatial modelling approaches.

Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function

Modelling the extremal dependence of bivariate variables is important in a wide variety of practical applications, including environmental planning, catastrophe modelling and hydrology. The majority of these approaches are based on the framework of bivariate regular variation, and a wide range of literature is available for estimating the dependence structure in this setting. However, such procedures are only applicable to variables exhibiting asymptotic dependence, even though asymptotic independence is often observed in practice. In this paper, we consider the so-called ‘angular dependence function’; this quantity summarises the extremal dependence structure for asymptotically independent variables. Until recently, only pointwise estimators of the angular dependence function have been available. We introduce a range of global estimators and compare them to another recently introduced technique for global estimation through a systematic simulation study, and a case study on river flow data from the north of England, UK.

Application of gene expression programing in predicting the concentration of PM2.5 and PM10 in Xi’an, China: a preliminary study

Introduction: Traditional statistical methods cannot find quantitative relationship from environmental data.Methods: We selected gene expression programming (GEP) to study the relationship between pollutant gas and PM2.5 (PM10). They were used to construct the relationship between pollutant gas and PM2.5 (PM10) with environmental monitoring data of Xi’an, China. GEP could construct a formula to express the relationship between pollutant gas and PM2.5 (PM10), which is more explainable. Back Propagation neural networks (BPNN) was used as the baseline method. Relevant data from January 1st 2021 to April 26th 2021 were used to train and validate the performance of the models from GEP and BPNN.Results: After the models of GEP and BPNN constructed, coefficient of determination and RMSE (Root Mean Squared Error) are used to evaluate the fitting degree and measure the effect power of pollutant gas on PM2.5 (PM10). GEP achieved RMSE of [8.7365–14.6438] for PM2.5; RMSE of [13.2739–45.8769] for PM10, and BP neural networks achieved average RMSE of [13.8741–34.7682] for PM2.5; RMSE of [29.7327–52.8653] for PM10. Additionally, experimental results show that the influence power of pollutant gas on PM2.5 (PM10) situates between −0.0704 and 0.6359 (between −0.3231 and 0.2242), and the formulas are obtained with GEP so that further analysis become possible. Then linear regression was employed to study which pollutant gas is more relevant to PM2.5 (PM10), the result demonstrates CO (SO2, NO2) are more related to PM2.5 (PM10).Discussion: The formulas produced by GEP can also provide a direct relationship between pollutant gas and PM2.5 (PM10). Besides, GEP could model the trend of PM2.5 and PM10 (increase and decrease). All results show that GEP can be applied smoothly in environmental modelling.