NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components DOI Creative Commons
Hongyi Li, Ting Yang, Lars Nerger

и другие.

Geoscientific model development, Год журнала: 2024, Номер 17(23), С. 8495 - 8519

Опубликована: Ноя. 29, 2024

Abstract. Identifying PM2.5 chemical components is crucial for formulating emission strategies, estimating radiative forcing, and assessing human health effects. However, accurately describing spatiotemporal variations in remains a challenge. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v1.0) that was suboptimal components. This paper introduces novel hybrid nonlinear DA (NAQPMS-PDAF v2.0) to interpret key (SO42-, NO3-, NH4+, OC, EC). NAQPMS-PDAF v2.0 improves upon v1.0 by effectively handling balancing stability nonlinearity DA, which achieved incorporating non-Gaussian distribution ensemble perturbation localized Kalman–nonlinear transform filter adaptive forgetting factor first time. The dependence tests demonstrate provides excellent results minimal size of 10, surpassing previous reports v1.0. A 1-month experiment shows analysis field generated good agreement observations, especially reducing underestimation NH4+ NO3- overestimation SO42-, EC. particular, Pearson correlation (CORR) values EC are above 0.96, R2 0.93. also demonstrates superior interpretation, most sites showing improvements over 50 %–200 % CORR %–90 RMSE five Compared poor performance global reanalysis dataset (CORR: 0.42–0.55, RMSE: 4.51–12.27 µg m−3) 0.35–0.98, 2.46–15.50 m−3), has highest 0.86–0.99 lowest 0.14–3.18 m−3. uncertainties examined, further highlighting potential advancing component studies.

Язык: Английский

Research on the Nonlinear Relationship Between Carbon Emissions from Residential Land and the Built Environment: A Case Study of Susong County, Anhui Province Using the XGBoost-SHAP Model DOI Creative Commons
Cheng Xu, Wei Xiong, Simin Zhang

и другие.

Land, Год журнала: 2025, Номер 14(3), С. 440 - 440

Опубликована: Фев. 20, 2025

Residential land is the basic unit of urban-scale carbon emissions (CEs). Quantifying and predicting CEs from residential are conducive to achieving urban neutrality. This study took 84 communities in Susong County, Anhui Province as its research object, exploring nonlinear relationship between built environment land. By identifying through building electricity consumption, 14 indicators, including area (LA), floor ratio (FAR), greening (GA), density (BD), gross (GFA), use mix rate (Phh), permanent population (PPD), were selected establish an interpretable machine learning (ML) model based on XGBoost-SHAP attribution analysis framework. The results show that, first, goodness fit XGBoost reached 91.9%, prediction accuracy was better than that gradient boosting decision tree (GBDT), random forest (RF), Adaboost model, traditional logistic model. Second, compared with other ML models, explained influencing factors more clearly. SHAP indicate BD, FAR, Phh most important affecting CEs. Third, there a significant threshold effect characteristic variables Fourth, interaction different dimensions environmental factors, played dominant role interaction. Reducing FAR considered be effective CE reduction strategy. provides practical suggestions for planners reducing land, which has policy implications significance.

Язык: Английский

Процитировано

1
Development of a multiple solution mixing mechanism based aerosol component retrieval method for polarimetric satellite measurements DOI
Ying Zhang, Chaoyu Yan, Zhiyuan Li

и другие.

Atmospheric Environment, Год журнала: 2025, Номер unknown, С. 121120 - 121120

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Using Open Data to Derive Parsimonious Data-Driven Models for Uncovering the Influence of Local Traffic and Meteorology on Air Quality: The Case of Madrid DOI
Koorosh Kazemi, Antón Vernet, Alexandre Fabregat

и другие.

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Unveiling the intricate dynamics of PM2.5 sulfate aerosols in the urban boundary layer: A pioneering two-year vertical profiling and machine learning-enhanced analysis in global Mega-City DOI
Hongyi Li, Ting Yang, Yifan Song

и другие.

Urban Climate, Год журнала: 2025, Номер 61, С. 102424 - 102424

Опубликована: Апрель 16, 2025

Язык: Английский

Процитировано

0
River total dissolved gas prediction using a hybrid greedy-stepwise feature selection and bidirectional long short-term memory model DOI Creative Commons
Khabat Khosravi, Salim Heddam, Changhyun Jun

и другие.

Ecological Informatics, Год журнала: 2025, Номер unknown, С. 103191 - 103191

Опубликована: Май 1, 2025

Язык: Английский

Процитировано

0
A Systemic Approach to the Product Life Cycle for the Product Development Process in Agriculture DOI Open Access
Franciele Lourenço, Marcelo Carneiro Gonçalves, Osíris Canciglieri

и другие.

Sustainability, Год журнала: 2024, Номер 16(10), С. 4207 - 4207

Опубликована: Май 17, 2024

For a long time, company’s Product Development Process (PDP) was seen as supporting the operations department, although PDP decisions and mistakes have considerable impact on market performance. This is critical even in agriculture where bad habits practices can lead rural producers to great losses. Therefore, this research investigates effect of performance products (bananas) southern region Brazil, based two analyses: (i) how sustainability support phases (ii) Life Cycle Assessment (LCA) mediate phases. study presents quantitative analysis using Confirmatory Factor Analysis (CFA) hierarchical ordinary least squares (OLS) regression data obtained from survey 110 who directly participate banana production planning process Brazil. Our results show that PDP, we confirm product development post-development phase has an In addition, identify pre-development dealing with (bananas), maturity stage LCA mediates sustainability. phase, conclude families develop economic environmental their products, which are growth may reduced results. As for when companies invest social practices, there complete mediation effect, these lose strength if introductory market. original matter, our contributes demonstrating value life cycle through systemic approach, filling gap literature due lack integrated areas seen.

Язык: Английский

Процитировано

1
ConvFormer-KDE: A Long-Term Point–Interval Prediction Framework for PM2.5 Based on Multi-Source Spatial and Temporal Data DOI Creative Commons
Shaofu Lin, Yuying Zhang,

Xingjia Fei

и другие.

Toxics, Год журнала: 2024, Номер 12(8), С. 554 - 554

Опубликована: Июль 30, 2024

Accurate long-term PM

Язык: Английский

Процитировано

0
Environmental Simulation Model Using System Dynamics to Estimate Air Pollution: A Case Study of Mexico City Metropolitan Area DOI Open Access
Héctor Manuel Godínez Cárdenas, Argelia Fabiola Miranda Pérez, Andrés Ramírez-Portilla

и другие.

Sustainability, Год журнала: 2024, Номер 16(19), С. 8359 - 8359

Опубликована: Сен. 26, 2024

Air pollution in megacities worldwide has been a severe public health and environmental problem; it contributes to climate change threatens life. Among all services, the transport sector accounts for most of these pollutants. However, despite strategies implemented reduce pollutants, mitigate their effects, promote prosperity sustainability, emission reduction targets remain unmet, causing average global temperatures keep increasing. In this study, air Mexico City Metropolitan Area (MCMA) is estimated through design an simulation model using system dynamics, which constitutes possibility authorities foresee evolution quality MCMA by assessing emissions from holistic perspective, based on region DESTEP analysis factors. Simulation results estimate more significant than predicted local government’s current forecast; would be up 106% lower PM10, 176% PM2.5, 34% NOx, 17% VOC. The conclusion demonstrated that one main factors with impact control use promotion transportation, along improvement its road infrastructure.

Язык: Английский

Процитировано

0
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components DOI Creative Commons
Hongyi Li, Ting Yang, Lars Nerger

и другие.

Geoscientific model development, Год журнала: 2024, Номер 17(23), С. 8495 - 8519

Опубликована: Ноя. 29, 2024

Abstract. Identifying PM2.5 chemical components is crucial for formulating emission strategies, estimating radiative forcing, and assessing human health effects. However, accurately describing spatiotemporal variations in remains a challenge. In our earlier work, we developed an aerosol extinction coefficient data assimilation (DA) system (Nested Air Quality Prediction Model System with the Parallel Data Assimilation Framework (NAQPMS-PDAF) v1.0) that was suboptimal components. This paper introduces novel hybrid nonlinear DA (NAQPMS-PDAF v2.0) to interpret key (SO42-, NO3-, NH4+, OC, EC). NAQPMS-PDAF v2.0 improves upon v1.0 by effectively handling balancing stability nonlinearity DA, which achieved incorporating non-Gaussian distribution ensemble perturbation localized Kalman–nonlinear transform filter adaptive forgetting factor first time. The dependence tests demonstrate provides excellent results minimal size of 10, surpassing previous reports v1.0. A 1-month experiment shows analysis field generated good agreement observations, especially reducing underestimation NH4+ NO3- overestimation SO42-, EC. particular, Pearson correlation (CORR) values EC are above 0.96, R2 0.93. also demonstrates superior interpretation, most sites showing improvements over 50 %–200 % CORR %–90 RMSE five Compared poor performance global reanalysis dataset (CORR: 0.42–0.55, RMSE: 4.51–12.27 µg m−3) 0.35–0.98, 2.46–15.50 m−3), has highest 0.86–0.99 lowest 0.14–3.18 m−3. uncertainties examined, further highlighting potential advancing component studies.

Язык: Английский

Процитировано

0