Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often proh...Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.展开更多
Fine dust particles （diameter is less than 2.5 μm） generated during machining processes,especially dry cutting,are harmful to operators,because they remain suspended in the air for long time and have marked concent...Fine dust particles （diameter is less than 2.5 μm） generated during machining processes,especially dry cutting,are harmful to operators,because they remain suspended in the air for long time and have marked concentration gradients in workshop.Hence studies about cutting dust source states and indoor air quality prediction have been developed.However,few researches focus on the distribution state of the cutting dust,dynamic status of fine dust particles,and environment estimating of the machining workshop.The machining workshops have diversified architectural structures,complex working conditions,so the dust emission is sensitive dynamic.According to these features,after analysis of the static and dynamic influence factors,this paper proposes a method and establishes a model to estimate the fine dust particles distribution based on COwZ （COMIS （conjunction of multizone infiltration specialists） with sub-zones） model when only dry cutting is processed just needing various working parameters.And two key technologies are discussed：the description of the machine tools using sub-zones of COwZ model considering the local obstacle effects of machine tools themselves;description and implementation of dynamic process of cutting dust emission with a new concept of equivalent source strengths.At last,multi-point experiments in a hybrid ventilation machining workshop prove the method is practical.Good agreement was observed between the estimation results and the experimental measurements for the investigated conditions.The proposed method can supply reference data for green manufacturing.展开更多
基金This research was supported by the MSIT(Ministry of Science and ICT),Korea,under the ICAN(ICT Challenge and Advanced Network of HRD)Program(IITP-2020-0-01816)supervised by the IITP(Institute of Information&Communications Technology Planning&Evaluation)This research was also supported by National Research Foundation(NRF)of Korea Grant funded by the Korean Government(MSIT)(No.2021R1A4A3022102).
文摘Given the increasing number of countries reporting degraded air quality,effective air quality monitoring has become a critical issue in today’s world.However,the current air quality observatory systems are often prohibitively expensive,resulting in a lack of observatories in many regions within a country.Consequently,a significant problem arises where not every region receives the same level of air quality information.This disparity occurs because some locations have to rely on information from observatories located far away from their regions,even if they may be the closest available options.To address this challenge,a novel approach that leverages machine learning and deep learning techniques to forecast fine dust concentrations was proposed.Specifically,continuous location features in the form of latitude and longitude values were incorporated into our models.By utilizing a comprehensive dataset comprising weather conditions,air quality measurements,and location properties,various machine learning models,including Random Forest Regression,XGBoost Regression,AdaBoost Regression,and a deep learning model known as Long Short-Term Memory(LSTM)were trained.Our experimental results demonstrated that the LSTM model outperforms the other models,achieving the best score with a root mean squared error of 23.48 in predicting fine dust(PM10)concentrations on an hourly basis.Furthermore,the fact that incorporating location properties,such as longitude and latitude values,enhances the overall quality of the regression models was discovered.Additionally,the implications and contributions of our research were discussed.By implementing our approach,the cost associated with relying solely on existing observatories can be substantially reduced.This reduction in costs can pave the way for economically efficient fine dust observation systems,ensuring more widespread and accurate air quality monitoring across different regions.
基金supported by National Natural Science Foundation of China (Grant No. 50775228)
文摘Fine dust particles （diameter is less than 2.5 μm） generated during machining processes,especially dry cutting,are harmful to operators,because they remain suspended in the air for long time and have marked concentration gradients in workshop.Hence studies about cutting dust source states and indoor air quality prediction have been developed.However,few researches focus on the distribution state of the cutting dust,dynamic status of fine dust particles,and environment estimating of the machining workshop.The machining workshops have diversified architectural structures,complex working conditions,so the dust emission is sensitive dynamic.According to these features,after analysis of the static and dynamic influence factors,this paper proposes a method and establishes a model to estimate the fine dust particles distribution based on COwZ （COMIS （conjunction of multizone infiltration specialists） with sub-zones） model when only dry cutting is processed just needing various working parameters.And two key technologies are discussed：the description of the machine tools using sub-zones of COwZ model considering the local obstacle effects of machine tools themselves;description and implementation of dynamic process of cutting dust emission with a new concept of equivalent source strengths.At last,multi-point experiments in a hybrid ventilation machining workshop prove the method is practical.Good agreement was observed between the estimation results and the experimental measurements for the investigated conditions.The proposed method can supply reference data for green manufacturing.
