How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form...How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.展开更多
Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, m...Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.展开更多
The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this st...The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this study,we investigate the use of first-person photos for the prediction of air quality.The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction.AirStackNet consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.展开更多
The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH fal...The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH falls below this level,the environment becomes dry,which can cause skin dryness,irritation,and discomfort at low temperatures.When the humidity level rises above 60%,a wet atmosphere develops,which encourages the growth of mold and mites.Asthma and allergy symptoms may occur as a result.Human health is harmed by excessive humidity or a lack thereof.Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere;certain models disinfect and purify the water,reducing the spread of bacteria.The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper.The Netatmo station was used to acquire the data needed in the application.The client is an Android application that allows the user to monitor air quality over a period of their choosing.For a good monitoring process,the Netatmo modules were used to collect data from both environments(indoor:temperature(T),RH,carbon dioxide(CO_(2)),atmospheric pressure(Pa),noise and outdoor:T and RH).The data is stored in a database,using MySQL.The Android application allows the user to view the evolution of the measured parameters in the form of graphs.Also,the paper presents a prediction model of RH using Azure Machine Learning Studio(Azure ML Studio).The model is evaluated using metrics:Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),Relative Absolute Error(RAE),Relative Squared Error(RSE)and Coefficient of Determination(CoD).展开更多
A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two re...A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two regulations where different ways of classification are taken into account. Parameters of the model are the initial and transition probabilities of the chain. They are estimated under the Bayesian point of view through samples generated directly from the corresponding posterior distributions. Using the estimated parameters, the probability of having an air quality index in a given hour of the day is obtained.展开更多
A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberran...A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberrant values or outliers due to the significant fluctuation of this sort of data, which is influenced by Climate change and the environment. With accelerating industrial expansion and rising population density in Kolkata City, air pollution is continuously rising. This study involves two phases, in the first phase imputation of missing values and second detection of outliers using Statistical Process Control (SPC), and Functional Data Analysis (FDA), studies to achieve the efficacy of the outlier identification methodology proposed with working days and Nonworking days of the variables NO<sub>2</sub>, SO<sub>2</sub>, and O<sub>3</sub>, which were used for a year in a row in Kolkata, India. The results show how the functional data approach outshines traditional outlier detection methods. The outcomes show that functional data analysis vibrates more than the other two approaches after imputation, and the suggested outlier detector is absolutely appropriate for the precise detection of outliers in highly variable data.展开更多
Atmospheric pollution is currently a real public health problem because of its potentially harmful effects on the environment as well as on human health. Several studies conducted in America, Europe, Asia, and Africa ...Atmospheric pollution is currently a real public health problem because of its potentially harmful effects on the environment as well as on human health. Several studies conducted in America, Europe, Asia, and Africa have established a significant link between air pollution and cancer, infertility, cardiovascular and respiratory morbidity, and mortality. This study aims to measure some automotive pollutants (CO, CO<sub>2</sub>, NO<sub>2</sub>, and SO<sub>2</sub>) by a selective and colorimetric method using a measurement system on Dräger reagent tubes coupled to a Dräger Accuro sampling pump in order to do a quantitative assessment of air quality in the nine districts of Brazzaville. The results obtained during this study revealed high concentration levels of pollutants (CO, CO<sub>2</sub>, NO<sub>2</sub>, SO<sub>2</sub>), all above the standards recommended by the WHO. The results obtained during this study made it possible to categorise Brazzaville as a polluted city.展开更多
The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.How...The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.However,the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring.Consequently,air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity.Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation.In this study,the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm(E-LCDGRA).LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways.The adapted variant,named E-LCDGRA,enhances the LCDGRA algorithm by incorporating a non-beacon-aided CSMA layer-2 un-slotted protocol with a back-off mechanism.The performance of the proposed E-LCDGRA is examined with other classical gathering schemes,including IEESEP and CERP,in terms of average lifetime,energy consumption,and delay.展开更多
Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for rep...Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.展开更多
Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities tu...Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.展开更多
Besides the need for low-cost instruments for air pollution measurement and detection,nowadays there are many concerns about air pollution due to the fast changes and used technologies.This research was applied using ...Besides the need for low-cost instruments for air pollution measurement and detection,nowadays there are many concerns about air pollution due to the fast changes and used technologies.This research was applied using an MQ2 gas detector,and microcontroller/Arduino-Uno.The design steps included bonding and connecting readymade sensors,coding,and finally testing the device.Testing has been conducted in Environment and Pollution Engineering Department laboratories,at the Technical Engineering College of Kirkuk.This study proposed the use of an MQ2 sensor for multi-gas rate detection which can exist indoors.The system uses also a DHT22 sensor for measuring environment temperature and humidity.The sensors are connected to Arduino and LCD to present data on LCD by powering the system with external power.Overall,the testing was conducted,and the device served as a measuring tool for indoor air as an accurate multi-gas rate detector.展开更多
Air pollution has far-reaching environmental and social consequences, requiring the active participation of individual citizens in improving air quality by means of emission-reducing behaviors. This research examines ...Air pollution has far-reaching environmental and social consequences, requiring the active participation of individual citizens in improving air quality by means of emission-reducing behaviors. This research examines the relationship between citizens’ knowledge, perceptions of air quality, attitudes towards policy measures, and intentions to adopt environmentally-friendly behaviors to combat air pollution. A comprehensive survey is conducted among a representative sample from seven regions in the Po basin area: Emilia-Romagna, Friuli-Venezia Giulia, Lombardy, Piedmont, Province of Trento, Valle d’Aosta, and Veneto. The survey aims at profiling participants based on their level of information, perceptions of air pollution, and attitudes towards emission-reducing behaviors. Cluster analysis identifies meaningful differences among citizen groups in terms of their awareness and intentions to engage in specific behaviors. Four distinct clusters emerge, each characterized by varying levels of willingness to embrace pro-environmental behaviors and support air quality improvement initiatives. By examining these profiles, the study uncovers patterns in citizens’ awareness, concerns, and acceptance of environmentally-friendly practices. The findings offer valuable insights for policymakers to develop targeted interventions, policies, and communication strategies.展开更多
Air pollution induces significant health risks to individuals exposed to high levels of pollutants concentration. For ground vehicles, pollutants infiltrate the car cabin through the ventilation system, leading to pot...Air pollution induces significant health risks to individuals exposed to high levels of pollutants concentration. For ground vehicles, pollutants infiltrate the car cabin through the ventilation system, leading to potential health issues. To address this problem, a project was undertaken to develop a protocol for characterizing in-cabin air quality. The study involved a closed chamber (the bubble) where its internal multiphase flow has been optimized to create controlled polluted atmospheres. Experiments were conducted to optimize the positioning of the stirring fan and particle generation source, ensuring a homogeneous distribution of fine and ultrafine particles. This study demonstrated the feasibility of implementing a platform dedicated to characterizing the vehicles’ in-cabin air quality under controlled conditions. It allows a better understanding of the dynamics of particle infiltration and the establishment of an optimized protocol for simultaneous measurements of indoor and outdoor concentrations.展开更多
Based on the automatic monitoring data of ambient air in Jinan City from 2013 to 2020,the changing trend and characteristics of air quality in Jinan City during 2013-2020 were analyzed by using the fuzzy comprehensive...Based on the automatic monitoring data of ambient air in Jinan City from 2013 to 2020,the changing trend and characteristics of air quality in Jinan City during 2013-2020 were analyzed by using the fuzzy comprehensive evaluation,air quality index(AQI)and ambient air quality comprehensive index methods.The three methods are different in principle,purpose of use,and characterization methods,but the conclusions are consistent.The ambient air quality in Jinan City was improved significantly from 2013 to 2020.The prime pollutants were mainly PM_(2.5)and PM_(10),but the impact on air quality declined,and the impact of O_(3)on air quality increased.The complex pollution characteristics were obvious.Air pollution was the most severe in winter and lighter in summer.展开更多
In this contribution, we use a coupled air quality modelling system (AQM) as a tool to design and develop an air quality plan in Madrid. AQM has allowed us to obtain a preliminary evaluation of the effect of mitigatio...In this contribution, we use a coupled air quality modelling system (AQM) as a tool to design and develop an air quality plan in Madrid. AQM has allowed us to obtain a preliminary evaluation of the effect of mitigation measures over regional and local air quality levels. To achieve these goals, we have prepared a sophisticated AQM, coupling the meteorological model WRF, the emission model AEMM, and the photochemical model CMAQ. AQM was evaluated using the whole modelling year 2010 working with high horizontal resolution, 3 km for the region of Madrid and 1km for urban metropolitan area of Madrid. Two different analyses have been realized: a source apportionment exercise following a zero-out methodology to obtain the contribution to the air quality levels of the different emission sector;and an evaluation of the main mitigation measures considered in the air quality plan using sensitivity analysis. The air quality plan was focused on the improvement of NO<sub>2</sub> levels and AQM analyzed the effect of the mitigation measures during ten episodes of 2011 where NO<sub>2</sub> or O<sub>3</sub> levels were the highest of the year;so we analyzed the effect of the mitigation plan in worst conditions. Results provided by the AQM system show that it accomplishes the European Directive modelling uncertainty requirements and the mean absolute gross error for 1-h maximum daily NO<sub>2</sub> is 31% over locations with higher levels of this atmospheric pollutant;the road traffic is the main contributor to the air quality levels providing a 81% for NO<sub>2</sub>, 67% for CO and 46% for PM<sub>10</sub>;measures defined in the plan achieve to reduce up to 11 μgm<sup>-3</sup> NO<sub>2</sub> levels offering highest reductions over urban areas with traffic influence.展开更多
Medium-term air quality assessment,benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes.By using daily and monthly averaged data,medium-term air quality...Medium-term air quality assessment,benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes.By using daily and monthly averaged data,medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives.By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark(CLAB).The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator.Such a benchmark can provide an overall air quality assessment for areas of interest.A case study is presented for Dallas County(U.S.A.)applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019.Six air pollutants considered are:ozone,carbon monoxide,nitrogen dioxide,sulfur dioxide,benzene and particulate matter less than 2.5 micrometres.These pollutants are assessed individually and in terms of CLAB,and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019.Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years.The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs.Conducting CLAB analysis on an ongoing basis,using a relevant near-past time interval for benchmarking that covers several years,can reveal useful monthly,seasonal and annual trends in overall air quality.This type of medium-term,benchmarked air quality data analysis is well suited for ecosystem monitoring.展开更多
Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring ...Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring stations installed in Xalapa and Minatitlan MZ in 2013-year. The methodology applied used quality criteria to the datasets, followed by data validation and statistics for further analysis to determine the hourly, weekly and yearly trends of NO<sub>2</sub>, O<sub>3</sub>, SO<sub>2</sub>, PM<sub>10</sub> and PM<sub>2.5</sub>. Indicators were compared with Mexican standards, CAI-LAC report, WHO guidelines, EU and USA standards to evaluate the AQ in both sites. We observed AQ trends from moderate to bad in Xalapa and Minatitlan MZ where the PM<sub>10</sub> and PM<sub>2.5</sub> surpassed the WHO guidelines and Mexican standards. O<sub>3</sub> and SO<sub>2</sub> in Xalapa presented a quality from good to moderate and in Minatitlan sometimes were from moderate to bad. NO<sub>2</sub> did not exceed the value limits of Mexican standards, only Xalapa has exceeded the WHO guidelines. In Minatitlan, the Mexican limits were not exceeded. Concluding, PM<sub>10</sub> and PM<sub>2.5</sub> concentrations were the main problem. Others pollutants that influenced the AQ were O<sub>3</sub>, NO<sub>2</sub> and SO<sub>2</sub> in Minatitlan MZ due probably to meteorology, site conditions, location and oil and petrochemical industries. In Xalapa, MZ NO<sub>2</sub> and SO<sub>2</sub> are attributed mainly to road transport.展开更多
ADMS-Urban is the most widely used advanced dispersion model for urban areas, being used extensively in China and worldwide, providing a practical tool for assessing and managing urban air quality. In this paper we br...ADMS-Urban is the most widely used advanced dispersion model for urban areas, being used extensively in China and worldwide, providing a practical tool for assessing and managing urban air quality. In this paper we briefly describe the ADMS dispersion models and give an overview of their use in China. And it describes in more detail the use of ADMS-Urban in Fushun in Liaoning province and in Jinan in Shangdong province respectively, for studies of urban air quality. Finally the conclusions are presented.展开更多
Along with the progress of urbanization and environ- mental deterioration, residents’ desire for improved air quality is increasing. In order to quantify an individual’s willingness-to-pay (WTP) for improved air qua...Along with the progress of urbanization and environ- mental deterioration, residents’ desire for improved air quality is increasing. In order to quantify an individual’s willingness-to-pay (WTP) for improved air quality in Jinan of eastern China, a contingent valuation method (CVM) was employed. A sample of 1,500 residents was chosen on the basis of multistage sampling methods with face-to-face interviews by using a series of hypo-thetical, open-ended scenario questions which were designed to elicit the respondents’ WTP. Results showed that 59.7% of respondents were able to express their WTP and the mean WTP is 100 Chinese Yuan (CNY) per person per year. A probit model on the probability of a positive WTP and a regression model were developed to find the relationship between endogenous variables and WTP. Most parameters in the econometric analysis had the expected sign. Annual household income and expenditure on treating respiratory diseases significantly influence WTP. The rates of positive WTP and the monetary amount are larger for men than for women. Results also showed that people who lived in more polluted areas were willing to pay more for clean air. Unlike developed countries, clean air may only be considered as a public good in China in that more than 40% of respondents had no incen- tive to bear the costs of attempting to achieve better air quality, which indicates the relatively low environmental consciousness.展开更多
Exposure to mining-induced particulate matter(PM)including coal dust and diesel particulate matter(DPM)causes severe respirat-ory diseases such as coal workers’pneumoconiosis(CWP)and lung cancer.Limited spatiotempora...Exposure to mining-induced particulate matter(PM)including coal dust and diesel particulate matter(DPM)causes severe respirat-ory diseases such as coal workers’pneumoconiosis(CWP)and lung cancer.Limited spatiotemporal resolution of current PM monitors causes miners to be exposed to unknown PM concentrations,with increased overexposure risk.Low-cost PM sensors offer a potential solution to this challenge with their capability in characterizing PM concentrations with high spatiotemporal resolution.However,their application in underground mines has not been explored.With the aim of examining the potential application of low-cost sensors in underground mines,a critical review of the present status of PM sensor research is conducted.The working principles of present PM monitors and low-cost sensors are com-pared.Sensor error sources are identified,and comprehensive calibration processes are presented to correct them.Evaluation protocols are pro-posed to evaluate sensor performance prior to deployment,and the potential application of low-cost sensors is discussed.展开更多
基金supported by the Institute of Information&Communications Technology Planning&Evaluation (IITP)grant funded by the Korean government (MSIT) (No.2022-0-00369)by the NationalResearch Foundation of Korea Grant funded by the Korean government (2018R1A5A1060031,2022R1F1A1065664).
文摘How can we efficiently store and mine dynamically generated dense tensors for modeling the behavior of multidimensional dynamic data?Much of the multidimensional dynamic data in the real world is generated in the form of time-growing tensors.For example,air quality tensor data consists of multiple sensory values gathered from wide locations for a long time.Such data,accumulated over time,is redundant and consumes a lot ofmemory in its raw form.We need a way to efficiently store dynamically generated tensor data that increase over time and to model their behavior on demand between arbitrary time blocks.To this end,we propose a Block IncrementalDense Tucker Decomposition(BID-Tucker)method for efficient storage and on-demand modeling ofmultidimensional spatiotemporal data.Assuming that tensors come in unit blocks where only the time domain changes,our proposed BID-Tucker first slices the blocks into matrices and decomposes them via singular value decomposition(SVD).The SVDs of the time×space sliced matrices are stored instead of the raw tensor blocks to save space.When modeling from data is required at particular time blocks,the SVDs of corresponding time blocks are retrieved and incremented to be used for Tucker decomposition.The factor matrices and core tensor of the decomposed results can then be used for further data analysis.We compared our proposed BID-Tucker with D-Tucker,which our method extends,and vanilla Tucker decomposition.We show that our BID-Tucker is faster than both D-Tucker and vanilla Tucker decomposition and uses less memory for storage with a comparable reconstruction error.We applied our proposed BID-Tucker to model the spatial and temporal trends of air quality data collected in South Korea from 2018 to 2022.We were able to model the spatial and temporal air quality trends.We were also able to verify unusual events,such as chronic ozone alerts and large fire events.
文摘Nairobi County experiences rapid industrialization and urbanization that contributes to the deteriorating state of air quality, posing a potential health risk to its growing population. Currently, in Nairobi County, most air quality monitoring stations use low-cost, inaccurate monitors prone to defects. The study’s objective was to map Nairobi County’s air quality using freely available remotely sensed imagery. The Air Pollution Index (API) formula was used to characterize the air quality from cloud-free Landsat satellite images i.e., Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI from Google Earth Engine. The API values were computed based on vegetation indices namely NDVI, TVI, DVI, and the SWIR1 and NIR bands on the QGIS platform. Qualitative accuracy assessment was done using sample points drawn from residential, industrial, green spaces, and traffic hotspot categories, based on a passive-random sampling technique. In this study, Landsat 5 API imagery for 2010 provided a reliable representation of local conditions but indicated significant pollution in green spaces, with recorded values ranging from -143 to 334. The study found that Landsat 7 API imagery in 2002 showed expected results with the range of values being -55 to 287, while Landsat 8 indicated high pollution levels in Nairobi. The results emphasized the importance of air quality factors in API calibration and the unmatched spatial coverage of satellite observations over ground-based monitoring techniques. The study recommends the recalibration of the API formula for characteristic regions, exploring newer satellite sensors like those onboard Landsat 9 and Sentinel 2, and involving key stakeholders in a discourse to develop a suitable Kenyan air quality index.
基金the Deputyship for Research and Innovation,Ministry of Education in Saudi Arabia for funding this research through project number PNU-DRI-RI-20-033.
文摘The quality of the airwe breathe during the courses of our daily lives has a significant impact on our health and well-being as individuals.Unfortunately,personal air quality measurement remains challenging.In this study,we investigate the use of first-person photos for the prediction of air quality.The main idea is to harness the power of a generalized stacking approach and the importance of haze features extracted from first-person images to create an efficient new stacking model called AirStackNet for air pollution prediction.AirStackNet consists of two layers and four regression models,where the first layer generates meta-data fromLight Gradient Boosting Machine(Light-GBM),Extreme Gradient Boosting Regression(XGBoost)and CatBoost Regression(CatBoost),whereas the second layer computes the final prediction from the meta-data of the first layer using Extra Tree Regression(ET).The performance of the proposed AirStackNet model is validated using public Personal Air Quality Dataset(PAQD).Our experiments are evaluated using Mean Absolute Error(MAE),Root Mean Square Error(RMSE),Coefficient of Determination(R2),Mean Squared Error(MSE),Root Mean Squared Logarithmic Error(RMSLE),and Mean Absolute Percentage Error(MAPE).Experimental Results indicate that the proposed AirStackNet model not only can effectively improve air pollution prediction performance by overcoming the Bias-Variance tradeoff,but also outperforms baseline and state of the art models.
基金supported by the Project“Entrepreneurial competences and excellence research in doctoral and postdoctoral programs-ANTREDOC”,project cofounded by the European Social Fund financing agreement No.56437/24.07.2019.
文摘The amount of moisture in the air is represented by relative humidity(RH);an ideal level of humidity in the interior environment is between 40%and 60%at temperatures between 18°and 20°Celsius.When the RH falls below this level,the environment becomes dry,which can cause skin dryness,irritation,and discomfort at low temperatures.When the humidity level rises above 60%,a wet atmosphere develops,which encourages the growth of mold and mites.Asthma and allergy symptoms may occur as a result.Human health is harmed by excessive humidity or a lack thereof.Dehumidifiers can be used to provide an optimal level of humidity and a stable and pleasant atmosphere;certain models disinfect and purify the water,reducing the spread of bacteria.The design and implementation of a client-server indoor and outdoor air quality monitoring application are presented in this paper.The Netatmo station was used to acquire the data needed in the application.The client is an Android application that allows the user to monitor air quality over a period of their choosing.For a good monitoring process,the Netatmo modules were used to collect data from both environments(indoor:temperature(T),RH,carbon dioxide(CO_(2)),atmospheric pressure(Pa),noise and outdoor:T and RH).The data is stored in a database,using MySQL.The Android application allows the user to view the evolution of the measured parameters in the form of graphs.Also,the paper presents a prediction model of RH using Azure Machine Learning Studio(Azure ML Studio).The model is evaluated using metrics:Mean Absolute Error(MAE),Root Mean Squared Error(RMSE),Relative Absolute Error(RAE),Relative Squared Error(RSE)and Coefficient of Determination(CoD).
文摘A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two regulations where different ways of classification are taken into account. Parameters of the model are the initial and transition probabilities of the chain. They are estimated under the Bayesian point of view through samples generated directly from the corresponding posterior distributions. Using the estimated parameters, the probability of having an air quality index in a given hour of the day is obtained.
文摘A variety of factors affect air quality, making it a difficult issue. The level of clean air in a certain area is referred to as air quality. It is challenging for conventional approaches to correctly discover aberrant values or outliers due to the significant fluctuation of this sort of data, which is influenced by Climate change and the environment. With accelerating industrial expansion and rising population density in Kolkata City, air pollution is continuously rising. This study involves two phases, in the first phase imputation of missing values and second detection of outliers using Statistical Process Control (SPC), and Functional Data Analysis (FDA), studies to achieve the efficacy of the outlier identification methodology proposed with working days and Nonworking days of the variables NO<sub>2</sub>, SO<sub>2</sub>, and O<sub>3</sub>, which were used for a year in a row in Kolkata, India. The results show how the functional data approach outshines traditional outlier detection methods. The outcomes show that functional data analysis vibrates more than the other two approaches after imputation, and the suggested outlier detector is absolutely appropriate for the precise detection of outliers in highly variable data.
文摘Atmospheric pollution is currently a real public health problem because of its potentially harmful effects on the environment as well as on human health. Several studies conducted in America, Europe, Asia, and Africa have established a significant link between air pollution and cancer, infertility, cardiovascular and respiratory morbidity, and mortality. This study aims to measure some automotive pollutants (CO, CO<sub>2</sub>, NO<sub>2</sub>, and SO<sub>2</sub>) by a selective and colorimetric method using a measurement system on Dräger reagent tubes coupled to a Dräger Accuro sampling pump in order to do a quantitative assessment of air quality in the nine districts of Brazzaville. The results obtained during this study revealed high concentration levels of pollutants (CO, CO<sub>2</sub>, NO<sub>2</sub>, SO<sub>2</sub>), all above the standards recommended by the WHO. The results obtained during this study made it possible to categorise Brazzaville as a polluted city.
文摘The air continues to be an extremely substantial part of survival on earth.Air pollution poses a critical risk to humans and the environment.Using sensor-based structures,we can get air pollutant data in real-time.However,the sensors rely upon limited-battery sources that are immaterial to be alternated repeatedly amid extensive broadcast costs associated with real-time applications like air quality monitoring.Consequently,air quality sensor-based monitoring structures are lifetime-constrained and prone to the untimely loss of connectivity.Effective energy administration measures must therefore be implemented to handle the outlay of power dissipation.In this study,the authors propose outdoor air quality monitoring using a sensor network with an enhanced lifetime-enhancing cooperative data gathering and relaying algorithm(E-LCDGRA).LCDGRA is a cluster-based cooperative event-driven routing scheme with dedicated relay allocation mechanisms that tackle the problems of event-driven clustered WSNs with immobile gateways.The adapted variant,named E-LCDGRA,enhances the LCDGRA algorithm by incorporating a non-beacon-aided CSMA layer-2 un-slotted protocol with a back-off mechanism.The performance of the proposed E-LCDGRA is examined with other classical gathering schemes,including IEESEP and CERP,in terms of average lifetime,energy consumption,and delay.
文摘Air quality is a critical concern for public health and environmental regulation. The Air Quality Index (AQI), a widely adopted index by the US Environmental Protection Agency (EPA), serves as a crucial metric for reporting site-specific air pollution levels. Accurately predicting air quality, as measured by the AQI, is essential for effective air pollution management. In this study, we aim to identify the most reliable regression model among linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression, and K-nearest neighbors (KNN). We conducted four different regression analyses using a machine learning approach to determine the model with the best performance. By employing the confusion matrix and error percentages, we selected the best-performing model, which yielded prediction error rates of 22%, 23%, 20%, and 27%, respectively, for LDA, QDA, logistic regression, and KNN models. The logistic regression model outperformed the other three statistical models in predicting AQI. Understanding these models' performance can help address an existing gap in air quality research and contribute to the integration of regression techniques in AQI studies, ultimately benefiting stakeholders like environmental regulators, healthcare professionals, urban planners, and researchers.
文摘Human living would be impossible without air quality. Consistent advancements in practically every aspect of contemporary human life have harmed air quality. Everyday industrial, transportation, and home activities turn up dangerous contaminants in our surroundings. This study investigated two years’ worth of air quality and outlier detection data from two Indian cities. Studies on air pollution have used numerous types of methodologies, with various gases being seen as a vector whose components include gas concentration values for each observation per-formed. We use curves to represent the monthly average of daily gas emissions in our technique. The approach, which is based on functional depth, was used to find outliers in the city of Delhi and Kolkata’s gas emissions, and the outcomes were compared to those from the traditional method. In the evaluation and comparison of these models’ performances, the functional approach model studied well.
文摘Besides the need for low-cost instruments for air pollution measurement and detection,nowadays there are many concerns about air pollution due to the fast changes and used technologies.This research was applied using an MQ2 gas detector,and microcontroller/Arduino-Uno.The design steps included bonding and connecting readymade sensors,coding,and finally testing the device.Testing has been conducted in Environment and Pollution Engineering Department laboratories,at the Technical Engineering College of Kirkuk.This study proposed the use of an MQ2 sensor for multi-gas rate detection which can exist indoors.The system uses also a DHT22 sensor for measuring environment temperature and humidity.The sensors are connected to Arduino and LCD to present data on LCD by powering the system with external power.Overall,the testing was conducted,and the device served as a measuring tool for indoor air as an accurate multi-gas rate detector.
文摘Air pollution has far-reaching environmental and social consequences, requiring the active participation of individual citizens in improving air quality by means of emission-reducing behaviors. This research examines the relationship between citizens’ knowledge, perceptions of air quality, attitudes towards policy measures, and intentions to adopt environmentally-friendly behaviors to combat air pollution. A comprehensive survey is conducted among a representative sample from seven regions in the Po basin area: Emilia-Romagna, Friuli-Venezia Giulia, Lombardy, Piedmont, Province of Trento, Valle d’Aosta, and Veneto. The survey aims at profiling participants based on their level of information, perceptions of air pollution, and attitudes towards emission-reducing behaviors. Cluster analysis identifies meaningful differences among citizen groups in terms of their awareness and intentions to engage in specific behaviors. Four distinct clusters emerge, each characterized by varying levels of willingness to embrace pro-environmental behaviors and support air quality improvement initiatives. By examining these profiles, the study uncovers patterns in citizens’ awareness, concerns, and acceptance of environmentally-friendly practices. The findings offer valuable insights for policymakers to develop targeted interventions, policies, and communication strategies.
文摘Air pollution induces significant health risks to individuals exposed to high levels of pollutants concentration. For ground vehicles, pollutants infiltrate the car cabin through the ventilation system, leading to potential health issues. To address this problem, a project was undertaken to develop a protocol for characterizing in-cabin air quality. The study involved a closed chamber (the bubble) where its internal multiphase flow has been optimized to create controlled polluted atmospheres. Experiments were conducted to optimize the positioning of the stirring fan and particle generation source, ensuring a homogeneous distribution of fine and ultrafine particles. This study demonstrated the feasibility of implementing a platform dedicated to characterizing the vehicles’ in-cabin air quality under controlled conditions. It allows a better understanding of the dynamics of particle infiltration and the establishment of an optimized protocol for simultaneous measurements of indoor and outdoor concentrations.
文摘Based on the automatic monitoring data of ambient air in Jinan City from 2013 to 2020,the changing trend and characteristics of air quality in Jinan City during 2013-2020 were analyzed by using the fuzzy comprehensive evaluation,air quality index(AQI)and ambient air quality comprehensive index methods.The three methods are different in principle,purpose of use,and characterization methods,but the conclusions are consistent.The ambient air quality in Jinan City was improved significantly from 2013 to 2020.The prime pollutants were mainly PM_(2.5)and PM_(10),but the impact on air quality declined,and the impact of O_(3)on air quality increased.The complex pollution characteristics were obvious.Air pollution was the most severe in winter and lighter in summer.
文摘In this contribution, we use a coupled air quality modelling system (AQM) as a tool to design and develop an air quality plan in Madrid. AQM has allowed us to obtain a preliminary evaluation of the effect of mitigation measures over regional and local air quality levels. To achieve these goals, we have prepared a sophisticated AQM, coupling the meteorological model WRF, the emission model AEMM, and the photochemical model CMAQ. AQM was evaluated using the whole modelling year 2010 working with high horizontal resolution, 3 km for the region of Madrid and 1km for urban metropolitan area of Madrid. Two different analyses have been realized: a source apportionment exercise following a zero-out methodology to obtain the contribution to the air quality levels of the different emission sector;and an evaluation of the main mitigation measures considered in the air quality plan using sensitivity analysis. The air quality plan was focused on the improvement of NO<sub>2</sub> levels and AQM analyzed the effect of the mitigation measures during ten episodes of 2011 where NO<sub>2</sub> or O<sub>3</sub> levels were the highest of the year;so we analyzed the effect of the mitigation plan in worst conditions. Results provided by the AQM system show that it accomplishes the European Directive modelling uncertainty requirements and the mean absolute gross error for 1-h maximum daily NO<sub>2</sub> is 31% over locations with higher levels of this atmospheric pollutant;the road traffic is the main contributor to the air quality levels providing a 81% for NO<sub>2</sub>, 67% for CO and 46% for PM<sub>10</sub>;measures defined in the plan achieve to reduce up to 11 μgm<sup>-3</sup> NO<sub>2</sub> levels offering highest reductions over urban areas with traffic influence.
文摘Medium-term air quality assessment,benchmarking it to recent past data can usefully complement short-term air quality index data for monitoring purposes.By using daily and monthly averaged data,medium-term air quality benchmarking provides a distinctive perspective with which to monitor air quality for sustainability planning and ecosystem perspectives.By normalizing the data for individual air pollutants to a standard scale they can be more easily integrated to generate a daily combined local area benchmark(CLAB).The objectives of the study are to demonstrate that medium-term air quality benchmarking can be tailored to reflect local conditions by selecting the most relevant pollutants to incorporate in the CLAB indicator.Such a benchmark can provide an overall air quality assessment for areas of interest.A case study is presented for Dallas County(U.S.A.)applying the proposed method by benchmarking 2020 data for air pollutants to their trends established for 2015 to 2019.Six air pollutants considered are:ozone,carbon monoxide,nitrogen dioxide,sulfur dioxide,benzene and particulate matter less than 2.5 micrometres.These pollutants are assessed individually and in terms of CLAB,and their 2020 variations for Dallas County compared to daily trends established for years 2015 to 2019.Reductions in benzene and carbon monoxide during much of 2020 are clearly discernible compared to preceding years.The CLAB indicator shows clear seasonal trends for air quality for 2015 to 2019 with high pollution in winter and spring compared to other seasons that is strongly influenced by climatic variations with some anthropogenic inputs.Conducting CLAB analysis on an ongoing basis,using a relevant near-past time interval for benchmarking that covers several years,can reveal useful monthly,seasonal and annual trends in overall air quality.This type of medium-term,benchmarked air quality data analysis is well suited for ecosystem monitoring.
文摘Mexico and currently in Veracruz state, there are metropolitan zones (MZ) growing. Therefore, main objective in this paper is to analyze new data and AQ trends during 01.09.2013 to 30.06.2015 of two new AQ monitoring stations installed in Xalapa and Minatitlan MZ in 2013-year. The methodology applied used quality criteria to the datasets, followed by data validation and statistics for further analysis to determine the hourly, weekly and yearly trends of NO<sub>2</sub>, O<sub>3</sub>, SO<sub>2</sub>, PM<sub>10</sub> and PM<sub>2.5</sub>. Indicators were compared with Mexican standards, CAI-LAC report, WHO guidelines, EU and USA standards to evaluate the AQ in both sites. We observed AQ trends from moderate to bad in Xalapa and Minatitlan MZ where the PM<sub>10</sub> and PM<sub>2.5</sub> surpassed the WHO guidelines and Mexican standards. O<sub>3</sub> and SO<sub>2</sub> in Xalapa presented a quality from good to moderate and in Minatitlan sometimes were from moderate to bad. NO<sub>2</sub> did not exceed the value limits of Mexican standards, only Xalapa has exceeded the WHO guidelines. In Minatitlan, the Mexican limits were not exceeded. Concluding, PM<sub>10</sub> and PM<sub>2.5</sub> concentrations were the main problem. Others pollutants that influenced the AQ were O<sub>3</sub>, NO<sub>2</sub> and SO<sub>2</sub> in Minatitlan MZ due probably to meteorology, site conditions, location and oil and petrochemical industries. In Xalapa, MZ NO<sub>2</sub> and SO<sub>2</sub> are attributed mainly to road transport.
文摘ADMS-Urban is the most widely used advanced dispersion model for urban areas, being used extensively in China and worldwide, providing a practical tool for assessing and managing urban air quality. In this paper we briefly describe the ADMS dispersion models and give an overview of their use in China. And it describes in more detail the use of ADMS-Urban in Fushun in Liaoning province and in Jinan in Shangdong province respectively, for studies of urban air quality. Finally the conclusions are presented.
基金The Natural Science Foundation of Shandong Prov-ince (Grant No. Y2005E01)The Environment Protection Science and Technology Project of Shandong Province (No. [2006] 025).
文摘Along with the progress of urbanization and environ- mental deterioration, residents’ desire for improved air quality is increasing. In order to quantify an individual’s willingness-to-pay (WTP) for improved air quality in Jinan of eastern China, a contingent valuation method (CVM) was employed. A sample of 1,500 residents was chosen on the basis of multistage sampling methods with face-to-face interviews by using a series of hypo-thetical, open-ended scenario questions which were designed to elicit the respondents’ WTP. Results showed that 59.7% of respondents were able to express their WTP and the mean WTP is 100 Chinese Yuan (CNY) per person per year. A probit model on the probability of a positive WTP and a regression model were developed to find the relationship between endogenous variables and WTP. Most parameters in the econometric analysis had the expected sign. Annual household income and expenditure on treating respiratory diseases significantly influence WTP. The rates of positive WTP and the monetary amount are larger for men than for women. Results also showed that people who lived in more polluted areas were willing to pay more for clean air. Unlike developed countries, clean air may only be considered as a public good in China in that more than 40% of respondents had no incen- tive to bear the costs of attempting to achieve better air quality, which indicates the relatively low environmental consciousness.
文摘Exposure to mining-induced particulate matter(PM)including coal dust and diesel particulate matter(DPM)causes severe respirat-ory diseases such as coal workers’pneumoconiosis(CWP)and lung cancer.Limited spatiotemporal resolution of current PM monitors causes miners to be exposed to unknown PM concentrations,with increased overexposure risk.Low-cost PM sensors offer a potential solution to this challenge with their capability in characterizing PM concentrations with high spatiotemporal resolution.However,their application in underground mines has not been explored.With the aim of examining the potential application of low-cost sensors in underground mines,a critical review of the present status of PM sensor research is conducted.The working principles of present PM monitors and low-cost sensors are com-pared.Sensor error sources are identified,and comprehensive calibration processes are presented to correct them.Evaluation protocols are pro-posed to evaluate sensor performance prior to deployment,and the potential application of low-cost sensors is discussed.
