Impact of Industrial Air Pollution on Agricultural Production

Wei, Wei; Wang, Zanxin

doi:10.3390/atmos12050639

Cited by 19 publications

(7 citation statements)

References 19 publications

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“…Major pollutants that cause such health problems include VOCs. In addition to affecting human health, Ap also affects agricultural productivity by disrupting plant biochemical reactions and causing soil degradation through acid rain [31].…”

Section: Applicationsmentioning

confidence: 99%

Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data

Biçer,

Bakouch,

Biçer

et al. 2024

Axioms

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In the vast statistical literature, there are numerous probability distribution models that can model data from real-world phenomena. New probability models, nevertheless, are still required in order to represent data with various spread behaviors. It is a known fact that there is a great need for new models with limited support. In this study, a flexible probability model called the unit Maxwell-Boltzmann distribution, which can model data values in the unit interval, is derived by selecting the Maxwell-Boltzmann distribution as a base-line model. The important characteristics of the derived distribution in terms of statistics and mathematics are investigated in detail in this study. Furthermore, the inference problem for the mentioned distribution is addressed from the perspectives of maximum likelihood, method of moments, least squares, and maximum product space, and different estimators are obtained for the unknown parameter of the distribution. The derived distribution outperforms competitive models according to different fit tests and information criteria in the applications performed on four actual air pollutant concentration data sets, indicating that it is an effective model for modeling air pollutant concentration data.

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Section: Applicationsmentioning

confidence: 99%

Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data

Biçer,

Bakouch,

Biçer

et al. 2024

Axioms

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“…These values were calculated by comparing real 24 h PM 2.5 concentration values with predicted values. The formulas for calculating the listed errors are given below in Equations ( 1)- (5).…”

Section: Assessment Of the Prediction Accuracymentioning

confidence: 99%

“…Research confirms that air pollution also affects animals, plants and other living organisms [1][2][3]. It can cause large losses in animal husbandry and crop yields, therefore causing losses in agriculture [4][5][6]. Pollution also has a direct or indirect adverse impact on other sectors of the economy [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

Hoffman,

Jasiński,

Baran

2024

Energies

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Various types of energetic fuel combustion processes emit dangerous pollutants into the air, including aerosol particles, marked as PM10. Routine air quality monitoring includes determining the PM10 concentration as one of the basic measurements. At some air monitoring stations, the PM10 measurement is supplemented by the simultaneous determination of the concentration of PM2.5 as a finer fraction of suspended particles. Since the PM2.5 fraction has a significant share in the PM10 fraction, the concentrations of both types of particles should be strongly correlated, and the concentrations of one of these fractions can be used to model the concentrations of the other fraction. The aim of the study was to assess the error of predicting PM2.5 concentration using PM10 concentration as the main predictor. The analyzed daily concentrations were measured at 11 different monitoring stations in Poland and covered the period 2010–2021. MLP (multilayer perceptron) artificial neural networks were used to approximate the daily PM2.5 concentrations. PM10 concentrations and time variables were tested as predictors in neural networks. Several different prediction errors were taken as measures of modeling quality. Depending on the monitoring station, in models with one PM10 predictor, the RMSE error values were in the range of 2.31–6.86 μg/m3. After taking into account the second predictor D (date), the corresponding RMSE errors were lower and were in the range of 2.06–5.54 μg/m3. Our research aimed to find models that were as simple and universal as possible. In our models, the main predictor is the PM10 concentration; therefore, the only condition to be met is monitoring the measurement of PM10 concentrations. We showed that models trained at other air monitoring stations, so-called foreign models, can be successfully used to approximate PM2.5 concentrations at another station.

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“…AP [1,2] is a matter of paramount concern to both the environment and public health, brought about by the contamination of air by chemical, physical, or biological agents. This deleterious phenomenon is known to have far-reaching implications in the agricultural industry [3], as it has been demonstrated to cause acid rain, reduced crop production, and inferior soil fertility. Notably, AP is a leading contributor to the global climate change crisis, resulting in more severe weather patterns across the globe [4].…”

Section: Introductionmentioning

confidence: 99%

A Collaborative Multi-Granularity Architecture for Multi-Source IoT Sensor Data in Air Quality Evaluations

Zhang

Cui

et al. 2023

Electronics

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Air pollution (AP) is a significant environmental issue that poses a potential threat to human health. Its adverse effects on human health are diverse, ranging from sensory discomfort to acute physiological reactions. As such, air quality evaluation (AQE) serves as a crucial process that involves the collection of samples from the environment and their analysis to measure AP levels. With the proliferation of Internet of Things (IoT) devices and sensors, real-time and continuous measurement of air pollutants in urban environments has become possible. However, the data obtained from multiple sources of IoT sensors can be uncertain and inaccurate, posing challenges in effectively utilizing and fusing this data. Meanwhile, differences in opinions among decision-makers regarding AQE can affect the outcome of the final decision. To tackle these challenges, this paper systematically investigates a novel multi-attribute group decision-making (MAGDM) approach based on hesitant trapezoidal fuzzy (HTrF) information and discusses its application to AQE. First, by combining HTrF sets (HTrFSs) with multi-granulation rough sets (MGRSs), a new rough set model, named HTrF MGRSs, on a two-universe model is proposed. Second, the definition and property of the presented model are studied. Third, a decision-making approach based on the background of AQE is constructed via utilizing decision-making index sets (DMISs). Lastly, the validity and feasibility of the constructed approach are demonstrated via a case study conducted in the AQE setting using experimental and comparative analyses. The outcomes of the experiment demonstrate that the presented architecture owns the ability to handle multi-source IoT sensor data (MSIoTSD), providing a sensible conclusion for AQE. In summary, the MAGDM method presented in this article is a promising scheme for solving decision-making problems, where HTrFSs possess excellent information description capabilities and can adequately describe indecision and uncertainty information. Meanwhile, MGRSs serve as an outstanding information fusion tool that can improve the quality and level of decision-making. DMISs are better able to analyze and evaluate information and reduce the impact of disagreement on decision outcomes. The proposed architecture, therefore, provides a viable solution for MSIoTSD facing uncertainty or hesitancy in the AQE environment.

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Impact of Industrial Air Pollution on Agricultural Production

Cited by 19 publications

References 19 publications

Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data

Unit Maxwell-Boltzmann Distribution and Its Application to Concentrations Pollutant Data

Regression Modeling of Daily PM2.5 Concentrations with a Multilayer Perceptron

A Collaborative Multi-Granularity Architecture for Multi-Source IoT Sensor Data in Air Quality Evaluations

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