摘要
机器人与机器学习的新闻编辑每日新闻-机器学习的新研究是一篇报道的主题。根据NewsRx记者在布拉兹州库里蒂巴的新闻报道，研究表明，“空气质量直接受到车辆污染物排放的影响，特别是在大城市和大都市地区，或者在没有对车辆排放标准进行合规性检查的情况下。微粒物质(PM)是内燃机燃料燃烧排放的污染物之一，仍然悬浮在大气中。对人口造成呼吸和心血管健康问题。新闻记者从研究中引用了一句话，“在这项研究中，我们分析了车辆排放、气象变量和低层大气中微粒浓度之间的相互作用，提出了预测和预报巴西库里蒂巴市PM2.5.气象和车辆流量数据的方法，采用随机森林(RF)和长短期记忆(LSTM)神经网络两种机器学习模型进行预测预报，选取预测基线模型作为多元线性回归(MLR)模型，进行预测。结果表明，在逐日和逐日预报尺度上，RF表现为T HAT，以行星边界层高度W为最重要变量，在逐日和逐日情况下，阵风和风速分别次之，日尺度上的PM预报精度最高(99.37%)。以前期资料5h为输入变量,采用LSTM模型进行1小时预报,准确率最高为99.71%,与MLR和NAVIE相比,RF和LSTM模型具有更好的预测效果。LSTM使用与COVID-19 Pa Ndemic时期（2020年和2021年）对应的数据进行训练，能够预测2022年PM2.5的浓度。数据表明，在PM2.5.浓度下，车辆的循环量较大，峰值较高，我们的结果有助于物理了解城市环境中影响车辆排放污染物扩散的因素。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Machine Learning is th e subject of a report. According to news reporting originating in Curitiba, Braz il, by NewsRx journalists, research stated, “Air quality is directly affected by pollutant emission from vehicles, especially in large cities and metropolitan a reas or when there is no compliance check for vehicle emission standards. Partic ulate Matter (PM) is one of the pollutants emitted from fuel burning in internal combustion engines and remains suspended in the atmosphere, causing respiratory and cardiovascular health problems to the population.” The news reporters obtained a quote from the research, “In this study, we analyz ed the interaction between vehicular emissions, meteorological variables, and pa rticulate matter concentrations in the lower atmosphere, presenting methods for predicting and forecasting PM2.5. Meteorological and vehicle flow data from the city of Curitiba, Brazil, and particulate matter concentration data from optical sensors installed in the city between 2020 and 2022 were organized in hourly an d daily averages. Prediction and forecasting were based on two machine learning models: Random Forest (RF) and Long Short-Term Memory (LSTM) neural network. The baseline model for prediction was chosen as the Multiple Linear Regression (MLR ) model, and for forecast, we used the naive estimation as baseline. RF showed t hat on hourly and daily prediction scales, the planetary boundary layer height w as the most important variable, followed by wind gust and wind velocity in hourl y or daily cases, respectively. The highest PM prediction accuracy (99.37% ) was found using the RF model on a daily scale. For forecasting, the highest ac curacy was 99.71% using the LSTM model for 1-h forecast horizon wi th 5 h of previous data used as input variables. The RF and LSTM models were abl e to improve prediction and forecasting compared with MLR and Naive, respectivel y. The LSTM was trained with data corresponding to the period of the COVID-19 pa ndemic (2020 and 2021) and was able to forecast the concentration of PM2.5 in 20 22, in which the data show that there was greater circulation of vehicles and hi gher peaks in the concentration of PM2.5. Our results can help the physical unde rstanding of factors influencing pollutant dispersion from vehicle emissions at the lower atmosphere in urban environment.”
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