Investigating the Air Quality Parameters in Louisiana’s Industrial Corridor: A Baton Rouge Case Study

In a local context, sustainable development entails utilizing the current resources—material and immaterial, measurable and immeasurable, popular and unpopular—of the community in a manner that avoids overexploitation and ensures intergenerational equity. This approach prioritizes the safety and health of local citizens, placing communal productivity above corporate profitability. This research aims to assess air quality surrounding 28 chemical industry sites in Baton Rouge, Louisiana, to understand the environmental and health impacts of industrial pollutants, with a focus on environmental justice. Air quality pollutants, including PM2.5, PM10, O3, NO2, CO, and SO2, were monitored for 75 days during the Summer, using the BreezoMeter app. Python, Mapize, and QGIS software technologies were utilized for data analysis and visualization. Findings indicate a reduction in NO2 and CO levels, compared to existing literature. However, the persistent challenge of particulate matter suggests areas for further environmental management efforts. Additionally, the research suggests a significant disparity in air pollution exposure, probably affecting marginalized communities. Although the nature of the study might not fully capture annual pollution trends, the findings highlight the urgent need for the chemical industry to adopt efficient production methods and for policymakers to enhance air quality standards and enforcement, particularly in pollution-sensitive areas. The disproportionate impact of air pollution on vulnerable communities calls for a more inclusive approach to environmental justice, ensuring equitable distribution of clean air benefits and community involvement in pollution management decisions.

Air Quality Monitoring and Its Implication on the Environmental Licensing Process in Brazil

In the state of Sao Paulo, Brazil, public policies regarding the air quality aimed at the welfare of the population are strongly dependent on monitoring conducted by the Sao Paulo State Environmental Company (CETESB), which can be influenced by faulty monitors and equipment support and cuts in power supply, among others. A research conducted from 1998 to 2008 indicated that a significant portion of the air quality automatic stations in the state of Sao Paulo did not meet the criterion of representativeness of measurements of PM10, NO2, O3, CO and SO2 concentrations which resulted in the classification of some municipalities as the nonattainment area, a situation evidenced for PM10 and O3 parameters. The network unavailability for each parameter was estimated and compared with the monitoring networks operated in Canada and the UK. This paper discusses the implications of the lack of representativeness of measurements in the environmental licensing process of pollution sources from 2008, when by the effect of state law, municipalities have been qualified according to their air quality nonattainment level.

Air quality nearby road traffic tunnel portals: BTEX monitoring

A monitoring campaign of BTEX （benzene, toluene, ethylbenzene, o- m- and p-xylene） was carried out nearby two tunnel portals in the urban area of Naples with the aim to verify air quality in this kind of urban sites. Sampling was carried out using the active adsorption technique. Sampling time was 1 h. Ambient temperature and traffic flow measurements were carried out during each sampling operation. The results indicate that average benzene concentrations at both sites exceed the limit value of 10 μg/Nm^3 established by the European Community （EC） （Dir. 2000/69）. Concentration levels of other BTEX are relatively high as well. A correlation between BTEX concentration and two wheeler vehicle flow was observed.

Optimization of Air Quality Monitoring Network Using GIS Based Interpolation Techniques

This paper proposes a simple method of optimizing Air Quality Monitoring Network (AQMN) using Geographical Information System (GIS), interpolation techniques and historical data. Existing air quality stations are systematically eliminated and the missing data are filled in using the most appropriate interpolation technique. The interpolated data are then compared with the observed data. Pre-defined performance measures root mean square error (RMSE), mean absolute percentage error (MAPE) and correlation coefficient (r) were used to check the accuracy of the interpolated data. An algorithm was developed in GIS environment and the process was simulated for several sets of measurements conducted in different locations in Riyadh, Saudi Arabia. This methodology proves to be useful to the decision makers to find optimal numbers of stations that are needed without compromising the coverage of the concentrations across the study area.

Implementation of the Air Quality Monitoring Network at Agadir City in Morocco

Air pollution is a major global issue with widely known harmful effects on human health and the environment. This pollution is a very complex phenomenon given the diversity of pollutants that may be present in the atmosphere. The air quality in urban areas is of a great concern for residents living in cities and represents a current issue that requires an adequate management. So that air quality policy is driven by health concerns. In this paper, we present an overview on the experience of Agadir city to establish the air quality management policy, local authority on the whole have developed a good understanding of air quality in the area. Indeed for several years, efforts have been made to monitor the air quality in this city, this translated by air quality assessment since 2006 using mobile laboratory and fixed station. Our goals in this study were to review the operation of Local Air Quality Management (LAQM) making better use of available resources to improve its outcomes and make recommendations with a view to improving air quality issues. This work highlights the requirement to revise periodically the LAQM for generating priority for air quality issues within local authority and the need to implement the optimizing Air Quality Monitoring Network (AQMN).

Low-Cost Sensors Calibration for Monitoring Air Quality in the Federal District—Brazil

Critical situations that cannot be solved by conventional approaches (traditional air quality monitoring networks), have the possibility of being managed quickly by a wide network of portable systems with sensors. The purpose of this research was to calibrate and validate low-cost sensors. Pilot indoor and outdoor areas, in the central area of Brasilia (Brazil’s capital city) were chosen for corporative performance evaluation of the sensors. The CO at 99.999% volumetric injection method has been used in a gas test box, among two MiCS-5521 (CO/VOC) sensors, one being new and the other one with a short useful life. The number of injections adopted to each volume (from 1 ml to 6 ml) was 10, rising each sensor’s confidence interval mean. A increase of the injected volume (ml) of CO resulted in significant decrease in a resistance (Ohms), as shown by a good inverse relationship on the interaction of these two variables (r = 0.88), with good measurement accuracy, when compared to the manufacturer’s reference datasheet. Finally, a geospatial management system was built for the pollution data measured by the low-cost sensors.

The Design of an Air Rower That Can Purify Indoor Air

To solve the problem that utilizing an air rower lowers indoor air quality due to the dust stirred up in the course of rowing, an air rower is designed to have the extra function of air purifying. The designed rower is composed of six parts, which are the frame, air generator, transmission part, air purifying filter cartridge, performance monitor, and electric motor. To fulfill the task, the filter cartridge is vertically arranged to lead the air to enter from the lower part and vent out of the upper part to filter and purify the air before it enters the generator so as that the indoor air is well circulated to improve the air purification effect when the rower is utilizing. The modular detachable design allows the air filter and electric motor to be installed and disassembled to adapt to different air purification needs. The designed rower has the benefit of lower purchasing cost and energy saving that may motivate exercising, bringing more fun and the sense of accomplishment.

Research on Decision Support System (DSS) of Atmospheric Environment Management in Anhui Province Based on Air Quality Forecasting

With the atmospheric stereoscopic monitoring, air quality forecasting and decision of environment management as the main line, and comprehensive management system as the guidance, five platforms including infrastructure, technological support, monitoring and early monitoring, decision support and information services were established. These platforms have 15 subsystems, including stereoscopic monitoring network, visual business consultation, high-performance computing environment, comprehensive management of atmospheric data, emission inventories of pollu-tion sources, evaluation tools of atmospheric models, monitoring and management of air pollution, forecasting and early warning of air quality, diag-nostic analysis of atmospheric environment, tracking of air pollution sources, emergency management of air pollution, conformity management of air quality, comprehensive display of information, releasing of information to external networks, and releasing of information by mobile networks. The decision support system （DSS） of atmospheric environment management could realize an integration business system of 11 air quality forecast - heavy pollution weather warning - diagnosis of pollution causes （dynamic analysis of pollution sources） -air quality conformity planning （air pollu-tion emergency management） -evaluation of forecasting and warning results （evaluation pf management measures） -air quality forecasting＂ and provide the technical support for the prevention and control of atmosphere pollution in Anhui province.

Big Data Analytics with Artificial Intelligence Enabled Environmental Air Pollution Monitoring Framework

Environmental sustainability is the rate of renewable resourceharvesting, pollution control, and non-renewable resource exhaustion. Airpollution is a significant issue confronted by the environment particularlyby highly populated countries like India. Due to increased population, thenumber of vehicles also continues to increase. Each vehicle has its individualemission rate;however, the issue arises when the emission rate crosses thestandard value and the quality of the air gets degraded. Owing to the technological advances in machine learning (ML), it is possible to develop predictionapproaches to monitor and control pollution using real time data. With thedevelopment of the Internet of Things (IoT) and Big Data Analytics (BDA),there is a huge paradigm shift in how environmental data are employed forsustainable cities and societies, especially by applying intelligent algorithms.In this view, this study develops an optimal AI based air quality prediction andclassification (OAI-AQPC) model in big data environment. For handling bigdata from environmental monitoring, Hadoop MapReduce tool is employed.In addition, a predictive model is built using the hybridization of ARIMAand neural network (NN) called ARIMA-NN to predict the pollution level.For improving the performance of the ARIMA-NN algorithm, the parametertuning process takes place using oppositional swallow swarm optimization(OSSO) algorithm. Finally, Adaptive neuro-fuzzy inference system (ANFIS)classifier is used to classify the air quality into pollutant and non-pollutant.A detailed experimental analysis is performed for highlighting the betterprediction performance of the proposed ARIMA-NN method. The obtainedoutcomes pointed out the enhanced outcomes of the proposed OAI-AQPCtechnique over the recent state of art techniques.

Assessment of Gaseous and Particulate Pollutants in the Ambient Air in Al Mirfa City, United Arab Emirates

From 2007-2009, National Energy and Water Research Center (NEWRC) on behalf of Abu Dhabi Water and Electricity Authority (ADWEA) conducted a long term baseline study of nitrogen dioxide (NO2), carbon monoxide (CO), sulfur dioxide (SO2), ozone (O3) and particulate matter 2, SO2, and 8 hour average of CO were within the acceptable levels of 400, 300 and 30,000 μg/m3 respectively, whereas the levels of O3 as 8 hour average (3) and PM10 (3) occasionally exceeded the FEA permissible limits during the study period. Seasonal variation based on three years data reveals that the highest concentration of NO2 and SO2 were during winter and for CO and O3 during summer months. Results indicate that the levels of SO2 and CO were significantly controlled and improved while the fuel combustion of Al Mirfa power plant had increased from 2007 to 2009. Dust has significantly impact on the air quality by elevated levels of PM10 exceed in several instances associated with regional sand- storm during the monitoring period.

Comprehensive evaluation method of urban air quality statistics based on environmental monitoring data and its application

Air quality monitoring is effective for timely understanding of the current air quality status of a region or city.Currently,the huge volume of environmental monitoring data,which has reasonable real-time performance,provides strong support for in-depth analysis of air pollution characteristics and causes.However,in the era of big data,to meet current demands for fine management of the atmospheric environment,it is important to explore the characteristics and causes of air pollution from multiple aspects for comprehensive and scientific evaluation of air quality.This study reviewed and summarized air quality evaluation methods on the basis of environmental monitoring data statistics during the 13th Five-Year Plan period,and evaluated the level of air pollution in the Beijing-Tianjin-Hebei region and its surrounding areas(i.e.,the“2+26”region)during the period of the three-year action plan to fight air pollution.We suggest that air quality should be comprehensively,deeply,and scientifically evaluated from the aspects of air pollution characteristics,causes,and influences of meteorological conditions and anthropogenic emissions.It is also suggested that a threeyear moving average be introduced as one of the evaluation indexes of long-term change of pollutants.Additionally,both temporal and spatial differences should be considered when removing confounding meteorological factors.

Application of Regularized Logistic Regression and Artificial Neural Network Model for Ozone Classification across El Paso County, Texas, United States

This paper focuses on ozone prediction in the atmosphere using a machine learning approach. We utilize air pollutant and meteorological variable datasets from the El Paso area to classify ozone levels as high or low. The LR and ANN algorithms are employed to train the datasets. The models demonstrate a remarkably high classification accuracy of 89.3% in predicting ozone levels on a given day. Evaluation metrics reveal that both the ANN and LR models exhibit accuracies of 89.3% and 88.4%, respectively. Additionally, the AUC values for both models are comparable, with the ANN achieving 95.4% and the LR obtaining 95.2%. The lower the cross-entropy loss (log loss), the higher the model’s accuracy or performance. Our ANN model yields a log loss of 3.74, while the LR model shows a log loss of 6.03. The prediction time for the ANN model is approximately 0.00 seconds, whereas the LR model takes 0.02 seconds. Our odds ratio analysis indicates that features such as “Solar radiation”, “Std. Dev. Wind Direction”, “outdoor temperature”, “dew point temperature”, and “PM10” contribute to high ozone levels in El Paso, Texas. Based on metrics such as accuracy, error rate, log loss, and prediction time, the ANN model proves to be faster and more suitable for ozone classification in the El Paso, Texas area.

室内空气调节系统的研究与设计

车间环境中,生产活动可导致空气质量恶化,极大影响员工身体健康。文章提出了一种空气调节系统,用于监测室内空气粉尘和VOC污染物浓度,适时开启换气系统,使空气参数保持在较高的水平。该系统集成多组粉尘和VOC检测模块和双向换气装置,根据发生空气质量恶化区域自适应调整空气流动路径,有针对性的净化空气。车间外设置风向传感器,根据自然风的风向调整室内换气方向,提高换气效率。各个监测点和执行点利用WiFi实现自组网,点与点之间密切配合,提高系统换气效率。验证试验表明:监测器能够稳定运行,点与点之间配合良好,高效率、针对性实现空气净化,大大改善了车间生产环境。

漏缝地板下粪水贮存周期内猪舍空气环境的监测

[目的]研究漏缝地板下粪水贮存周期内猪舍空气环境,为猪健康生长和生产提供参考。[方法]选取1栋分娩猪舍,测定粪水贮存周期(21 d)内猪舍的空气环境指标。沿猪舍对角线方向均匀选5个测定点,粪水贮存周期内每3 d测1次,每次分别在6:00、11:00、13:00、18:00测定。[结果]猪舍粪水贮存周期内平均温度(26.08±1.45)℃,相对湿度(93.44±3.96)%,氨气质量浓度为(3.58±1.62)mg/m^(3),二氧化碳质量浓度为(1760.63±284.98)mg/m^(3)。第18天的温度、相对湿度、二氧化碳浓度最高,各指标基本与其他测定时间差异显著;第21天氨气浓度最高,与第3天、第6天差异显著;[结论]漏缝地板下粪水贮存周期内,温度略高,相对湿度过高,氨气浓度持续上升但未超过标准,二氧化碳浓度较高,应增加通风设备,改善猪舍空气环境。

移动监测技术在大气环境研究中的应用

移动监测是把用于环境监测的设备安装在移动载体(汽车、自行车、无人机和船等)上,开展环境质量监测的技术。移动监测技术以其灵活、机动、及时等优势,近年来被广泛应用于大气环境监测中,但针对移动监测技术在大气环境研究中的应用场景及其存在的优缺点等缺少系统性研究。通过文献调研的方式对国内外移动监测技术在大气环境研究中的应用进行综述,重点讨论了监测设备及其搭载平台。将移动监测设备划分为传感器和大型设备2类,其中传感器以体积小、成本低等优势被大量应用在无人机、汽车等平台中。大型设备则包括监测仪和分析仪,但由于受到体积和成本的限制多被应用在飞机和船等面积较大的平台中。根据大气环境移动监测设备搭载的平台将其划分为车载移动监测、机载移动监测和船载移动监测三大类,其中车载移动监测主要用于环境质量监测、溯源分析和热点监测、汽车尾气排放监测及空气污染暴露监测等,机载移动监测主要用于环境空气质量监测、溯源分析和热点监测及大气污染物传输监测,船载移动监测主要用于海表大气组分、溯源分析和船舶尾气排放等研究。最后讨论了移动监测平台现有应用中存在的问题(例如覆盖率、续航、数据质量等),并对未来研究进行了展望。

负氧离子技术在汽车布置上的应用研究

近些年,空气质量状况得到了人们的广泛关注和重视。有调研结果显示,汽车车内空间是一个空气质量状况相当恶劣的场所之一,而人们使用汽车已经变得越来越普遍,这就对汽车内的空气质量提出了更多更高的要求。首先,文章介绍了负氧离子技术的作用和原理,特别是对负氧离子技术在汽车内的布置应用进行了研究分析;其次,主要是在车内不同位置布置不同数量的负氧离子发生器,并测试空调出风口和驾乘人员头部区域的负氧离子数量,以此获得负氧离子净化效果与布置位置及数量的关系;最后,测试在选用不同的空调风量和模式的情况下,空调出风口和驾乘人员头部区域的负氧离子数量,可以获得负氧离子浓度与空调风量及模式的关系。研究结果表明,负氧离子发生器布置在空调吹面出风口时,可以带来较好的车内空气质量净化效果,而使用较大风量吹面则会带来较高浓度的负氧离子。该研究结果为负氧离子技术在汽车内的布置应用提供了依据,具有一定的实用价值。

基于通道剪枝的轻量化空气质量检测方法

针对传统空气质量检测系统结构复杂、部署困难以及成本较高的问题,利用图卷积网络对大气图像特征进行分析,提出了一种基于通道剪枝的轻量化空气质量检测算法。首先以ResNet50为基础网络训练一个PM 2.5指数检测网络,实现了空气质量初步的自动化检测。然后对网络模型中的所有卷积核通道和相关的参数传递进行图节点核权重边建模,以图表示形式输入GCN,并输出针对每个卷积核节点的剪枝重要性判别预测。最后根据GCN结果进行通道剪枝,使用原始数据集对剪枝后模型的参数进行微调,在保持网络检测精准度的情况下,实现网络模型的轻量化。通过对比实验和消融实验验证了提出的检测方法具有较高的检测精度,平均检测误差仅有5.31%,RMSE提升了0.52,R-square仅降低了0.018,解决了网络模型的参数量和计算量过大的问题,网络参数量从4.12×10^(7)降低至2.01×10^(7),FPS从16.78帧/s提升至30.9帧/s,为在便携式终端上实现空气质量检测任务提供了有力的技术支持。

基于STM32和Android的智能家居系统设计

为设计可通过Android智能手机控制的智能家居系统,以STM32F407ZGT6单片机为控制中心,使用PM2.5传感器GP2Y1051A、烟雾与可燃气体传感器MQ2、硫化物与苯系等有害气体传感器MQ135、气压传感器BMP180、数字温度传感器DS18B20和光敏电阻等完成对空气质量、气压、温度和光照度等的检测,使用4.3寸TFT电容触摸彩屏实现人机交互,由Wi-Fi模块连接远程因特网云服务器。实验证明,操作者能通过Android应用程序远程实时监控家居系统;紧急情况下,可经过GSM网络在5 s内给使用者手机发送报警短消息。系统具有性能良好,操作简单方便,成本低廉的优点,适合在智能化市场上推广应用。