A Particulate Matter Concentration Prediction Model Based on Long Short-Term Memory and an Artificial Neural Network.

Regional air quality over East Asia, including South Korea, has recently become a center of public attention because of a few episodes of very high particulate matter (PM) concentrations. Predicting PM variation with lead time of a few hours up to days is one of the key areas that the governments are working on because it can benefit from an early warning system to short‐term mitigation effort. In this study, the influence of synoptic weather conditions on regional air quality was investigated with the occurrence frequencies of PM episodes as a function of various synoptic weather patterns during winter and spring. During winter, dry moderate (DM) types occur frequently alongside high PM cases (24 hr mean PM10 concentration >). The composite weather map showed a weak northwesterly wind field as a potential cause. On the contrary, it is interesting to note that dry polar types can be associated with low PM cases (24 hr mean PM10 concentration <) as well as high PM depending on near‐surface wind speed. Furthermore, DM and dry tropical types were found to be highly correlated with high (higher) PM concentrations during spring season, likely because of the enhanced static stability in the lower troposphere. It should be noted that PM concentration depends on the lower atmospheric stability. The close relationship between synoptic weather patterns and PM concentration suggests that synoptic weather can play an important role in regional air quality.

The objectives of the present study were to investigate particulate matter (PM) and polycyclic aromatic hydrocarbon (PAH) concentrations in ambient air during rice straw open burning and non-open burning periods. In the ambient air of a rice field, the mean PM concentration during and after an open burning event were 1828 and 102 μg m−3, respectively, which demonstrates that during a rice field open burning event, the PM concentration in the ambient air of rice field is over 17 times higher than that of the non-open burning period. During an open burning event, the mean total PAH and total toxic equivalence (BaP eq ) concentrations in the ambient air of a rice field were 7206 ng m−3 and 10.3 ng m−3, respectively, whereas after the open burning event, they were 376 ng m−3 and 1.50 ng m−3, respectively. Open burning thus increases total PAH and total BaP eq concentrations by 19-fold and 6.8-fold, respectively. During a rice straw open burning event, in the ambient air of a rice field, the mean dry deposition fluxes of total PAHs and total BaP eq were 1222 μg m−2 day−1 and 4.80 μg m−2 day−1, respectively, which are approximately 60- and 3-fold higher than those during the non-open burning period, respectively. During the non-open burning period, particle-bound PAHs contributed 79.2–84.2% of total dry deposition fluxes (gas + particle) of total PAHs. However, an open burning event increases the contribution to total PAH dry deposition by particle-bound PAHs by up to 85.9–95.5%. The results show that due to the increased amount of PM in the ambient air resulting from rice straw open burning, particle-bound PAHs contributed more to dry deposition fluxes of total PAHs than they do during non-open burning periods. The results show that biomass (rice straw) open burning is an important PAH emission source that significantly increases both PM and PAH concentration levels and PAH dry deposition in ambient air.

Air pollution has become a major environmental and public health concern, particularly in urban areas where rapid industrialization, traffic emissions, and population density contribute to elevated levels of airborne contaminants. In educational settings, such as secondary schools, poor air quality can adversely affect the health and cognitive performance of students, making the assessment of indoor and outdoor pollutants essential for risk management. This study comprehensively assessed the levels of particulate matter concentrations in indoor and outdoor air at some secondary schools in Port Harcourt Local Government Area (LGA), Rivers State. The study focused on measuring levels of particulate matter (PM1, PM2.5, PM4, PM7, PM10) and evaluating seasonal variations. Sample collection across nine secondary schools covered a period of twelve months (twice per term during academic session), covering the dry and wet seasons. Particulate matter concentrations were obtained using a calibrated handheld Aerocet Particle Mass Counter 531S. The results demonstrated higher indoor particulate matter (PM) concentrations compared to outdoors across schools. Indoor PM1 ranged from 3.9–8.1 µg/m³ versus 2.90–7.45 µg/m³ outdoors, while PM2.5 was 18.5–24.5 µg/m³ indoors compared to 13.3–20.07 µg/m³ outdoors. The coarser particles (PM4, PM7, and PM10) also exhibited higher concentrations indoors compared to outdoors with location PCSN having the highest average concentration indoor for PM10 at 153.9±118.5 µg/m³. Overall, particulate levels tend to be higher indoors than outdoors, especially for larger particles, although some outdoor values remain comparably high in certain locations. Overall, particulate matter concentrations were consistently higher during the dry season than wet season across all schools and particulate sizes, with larger particles exhibiting more pronounced increase. Generally, the concentrations of particulate matter for all nine schools were within permissible limit from PM1-PM10 (both indoor and outdoor) except location PCSN school which exceeded the permissible limit at PM10 for indoor. The study highlighted significant indoor particulate accumulation, across schools within Port Harcourt L.G A. It was recommended that schools authorities should implement improved ventilation systems, limit indoor combustion sources, and establish regular cleaning and dust control practices to reduce indoor particulate matter.

Hybrid intelligent system for air quality forecasting using phase adjustment

The particulate matter (PM) concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth’s atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governments to alert the population. Forecasting systems based on Artificial Neural Networks (ANNs) have been highlighted in the literature due to their performances. In general, three ANN-based approaches have been found for this task: ANN trained via learning algorithms, hybrid systems that combine search algorithms with ANNs, and hybrid systems that combine ANN with other forecasters. Independent of the approach, it is common to suppose that the residuals (error series), obtained from the difference between actual series and forecasting, have a white noise behavior. However, it is possible that this assumption is infringed due to: misspecification of the forecasting model, complexity of the time series or temporal patterns of the phenomenon not captured by the forecaster. This paper proposes an approach to improve the performance of PM forecasters from residuals modeling. The approach analyzes the remaining residuals recursively in search of temporal patterns. At each iteration, if there are temporal patterns in the residuals, the approach generates the forecasting of the residuals in order to improve the forecasting of the PM time series. The proposed approach can be used with either only one forecaster or by combining two or more forecasting models. In this study, the approach is used to improve the performance of a hybrid system (HS) composed by genetic algorithm (GA) and ANN from residuals modeling performed by two methods, namely, ANN and own hybrid system. Experiments were performed for PM2.5 and PM10 concentration series in Kallio and Vallila stations in Helsinki and evaluated from six metrics. Experimental results show that the proposed approach improves the accuracy of the forecasting method in terms of fitness function for all cases, when compared with the method without correction. The correction via HS obtained a superior performance, reaching the best results in terms of fitness function and in five out of six metrics. These results also were found when a sensitivity analysis was performed varying the proportions of the sets of training, validation and test. The proposed approach reached consistent results when compared with the forecasting method without correction, showing that it can be an interesting tool for correction of PM forecasters.

Indoor air quality (IAQ) in workplaces significantly impacts occupational health and productivity, necessitating comparative evaluations across diverse environments. This study investigates particulate matter (PM) concentrations in indoor academic workspaces in Chennai, India, and Canberra, Australia, using the GRIMM 11D aerosol spectrometer. A 12-hour monitoring campaign was done in IIT Madras, India, and ANU, Australia for 3 days during the post-winter (Spring) season. It measured the PM1, PM2.5, PM4, and PM10 levels in the indoor workspaces offering insights into PM concentrations.The average PM concentrations in Chennai were significantly higher than in Canberra. Indian workplaces recorded PM1, PM2.5, PM4, and PM10 levels of 4.03±1.09, 8.28±2.15, 12.36±4.26, and 15.83±6.43 (µg/m³), respectively. Corresponding Australian values were notably lower, at 1.53±0.47, 2.82±1.02, 3.52±1.69, and 4.14±2.72 (µg/m³), respectively. Spikes in PM10 levels in both regions suggest occasional localized pollution events or episodic pollutant intrusions, influencing PM concentrations. Additionally, the fine PM fractions (PM1 and PM2.5) were more prominent in Canberra, indicating potential variations in pollutant sources and infiltration rates.Health risk assessments were performed by simulating lung deposition dosages for males and females using the ‘Symmetric Lung’ configuration within the Multiple Path Dosimetry Model (MPPD). The model revealed stark contrasts in PM lung deposition doses between the two regions, with Indian workplaces presenting significantly higher health risks. In Chennai, male dosages for PM1, PM2.5, PM4, and PM10 were 4.23, 22.16, 38.98, and 56.41 µg, respectively, while females experienced slightly lower dosages of 2.79, 12.99, 23.33, and 34.70 µg. In Canberra, the respective values for males were 1.61, 5.80, 9.14, and 12.33 µg, and for females, 1.05, 3.40, 5.41, and 7.46 µg. These findings highlight a significantly higher health risk for workers in Chennai, with females in both locations receiving lower doses due to smaller lung capacities and breathing rates.This pilot study brings out substantial regional differences in IAQ, shaped by environmental factors, building ventilation standards, and external pollutant sources and infiltration rates. Elevated PM concentrations in Chennai signal a pressing need for interventions to enhance workplace air quality, such as improved filtration and ventilation systems and awareness campaigns. Meanwhile, the finer PM fraction in Canberra warrants attention due to its deeper penetration into the respiratory tract and long-term health implications.Further research should address long-term exposure risks, seasonal variability, and effective mitigation strategies to improve IAQ and safeguard academic workforce health in diverse geographical settings.

Nowadays, air quality is a major issue, especially in large cities. Apart from air pollution, particulate matter (PM), especially PM2.5, poses serious health risks to individuals with respiratory conditions. Accurate forecasting of PM levels is crucial to warn vulnerable populations and reduce exposure. Machine learning models can effectively predict PM concentrations based on historical data and barometric conditions such as temperature and humidity. Such predictions can support timely public health interventions and environmental policy decisions. The selection of the optimal machine learning model for time series forecasting requires a careful balance between predictive accuracy and computational efficiency. This study evaluates a number of widely used models, such as Random Forest (RF), Long Short-Term Memory (LSTM), Convolutional Neural Network-LSTM (CNN–LSTM), Extreme Gradient Boosting (XGB/HistGradientBoosting), and hybrid approaches (LSTM embeddings + RF), in the context of time series forecasting for particulate matter (PM) concentrations. Performance is assessed using three key error metrics: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Scaled Error (MASE). Additionally, the computational demands and development complexity of each model are analyzed. The overall results are of great interest for each application model, and in more detail, it is shown that the best compromise between accuracy and efficiency can be achieved, while a corresponding prediction model with satisfactory predictive performance can be implemented. The results show that CNN–LSTM and hybrid approaches provide high accuracy, while tree-based models are computationally efficient, offering practical options for real-time forecasting systems.

With increasing global concerns related to global warming, air pollution, and environmental health, South Korea is actively implementing various particulate matter (PM) reduction policies to improve air quality. Accurate data analysis, including the investigation of weather phenomena, monitoring, and integrated prediction, is essential for effective PM reduction. However, the factors influencing the PM generated from domestic road sections have not yet been systematically analyzed, and currently, no predictive models utilize weather and traffic data. This study analyzed the correlations among factors influencing PM to develop models for estimating fine and coarse PM (PM2.5 and PM10, respectively) concentrations in road sections. Regression analysis models were used to assess the sensitivity of PM2.5 and PM10 concentrations to the traffic volume, whereas machine learning-based models, including linear regression, convolutional neural networks, and random forest models, were constructed and compared. The random forest models outperformed the other models, with coefficients of determination of 0.74 and 0.71 and mean absolute errors of 5.78 and 9.60 for PM2.5 and PM10, respectively. These results indicate that the random forest model provides the most accurate PM concentration estimates for road sections. The practical applications of the developed models were considered to inform effective transportation policies aimed at reducing PM. The developed model has practical applications in the formulation of transportation policies aimed at reducing PM. In particular, the model will play an important role in data-driven policymaking for sustainable urban development and environmental protection. By analyzing the correlation between traffic volume and weather conditions, policymakers can formulate more effective and sustainable strategies for reducing air pollution.

Systematic review of UAV-assisted airborne particulate matter measurement in urban areas

Investigating the impact of relative humidity and air tightness on PM sedimentation and concentration reduction

Optimization of neural network parameters in improvement of particulate matter concentration prediction of open-pit mining

HighlightsParticulate matter (PM) data were analyzed to identify PM emission characteristics among different animal types.The PM concentrations were higher in broiler chicken and swine farrowing houses and were higher in winter.The PM emissions were also higher in broiler chicken houses and swine farrowing rooms.The PM in the layer chicken house in Indiana had narrower distributions with a greater percentage of smaller particles.Abstract. Understanding the characteristics of particulate matter (PM) emissions from animal feeding operations (AFOs) is essential to address the associated health and environmental impacts and to develop control strategies to mitigate such impacts. This article reports a study of PM concentrations and emission characteristics from 26 poultry and swine production houses to investigate the similarities and differences in PM emission characteristics, e.g., concentrations, emission rates, and particle size distribution (PSD), among different animal and housing types. Concentration and emission data for PM2.5, PM10, and total suspended particulates (TSP) collected by the National Air Emission Monitoring Study (NAEMS) were used to compare the differences among different production practices and animal types. The PSDs of the PM were examined based on the PM2.5/PM10 and PM10/TSP emission rate ratios. It was discovered that the concentrations of PM varied among animal types. For poultry, the concentrations of PM were higher in broiler houses than in other poultry houses. For swine, the average concentrations of PM were higher in farrowing rooms than in swine barns. Moreover, the PM concentrations in poultry and swine houses exhibited significant seasonal trends, with higher concentrations in winter and lower concentrations in summer, which were in a reverse relationship with ventilation rates. The PM emissions also varied among animal types. For poultry, the PM emissions were significantly higher for poultry production houses in California. For swine, the PM emissions were significantly higher for farrowing rooms than other swine houses. The PSD of PM varied among animal types, with mass median diameters (MMD) in the ranges of 6.51 to 13.62 µm for poultry houses and 7.94 to 17.19 µm for swine houses. The geometric standard deviations (GSD) were in the ranges of 1.66 to 2.71 and 1.65 to 2.9 for poultry and swine PM, respectively. The PM in the layer house in Indiana had a narrower distribution (smaller GSD) with a greater percentage of smaller particles than the other poultry houses, while the PM in the broiler house in California had a broader distribution (larger GSD) than the other poultry houses. For swine, the PM in the sow barn in North Carolina had a narrower distribution (smaller GSD) than the other swine houses, while the PM in the farrowing houses in Oklahoma had a broader distribution (larger GSD) than the other swine houses. The knowledge gained from this research may provide insights for addressing the PM emissions from various animal production systems. Keywords: Concentration and emission, Particulate matter, PM2.5, PM10, Poultry, swine, PSD, TSP.

The present investigation introduces a robust soft computing model by comparing twelve least square support vector machine (LSSVM), six long short-term memory (LSTM), and thirty-six artificial neural network (ANN) models to predict the unconfined compressive strength (UCS) of fine-grained soil. For that purpose, a database of fine content, dry unit weight, porosity, void ratio, degree of saturation, and specific gravity results of 85 soil specimens has been compiled from the literature. 75 and 10 soil specimens were trained and tested for each model. Six training databases have been prepared to analyze the effect of quality and quantity of training database by selecting 50%, 60%, 70%, 80%, 90%, and 100% of 75 soil specimens. The performance comparison demonstrated that the LSTM model (MD 113) requires fewer training datasets (50% of 75) than the LSSVM (MD 102 and MD 108) and ANN (MD 120, MD 127, MD 136, MD139, MD 148, and MD 150) models. Also, it was observed that the nonlinear LSSVM model (MD 108) is unaffected by multicollinearity in training datasets and predicted UCS better than the linear LSSVM model (MD 102). Furthermore, the Levenberg-Marquardt neural network model (MD 120) has outperformed the other ANN models with the root mean square error (RMSE) of 5.1214 N/cm2, the mean absolute error (MAE) of 4.1379 N/cm2, and correlation (R) of 0.9836. The overall performance comparison revealed that the LSTM model is more potent than the LSSVM and ANN models. The LSTM model predicted the UCS of fine-grained soil with the RMSE of 4.7539 N/cm2, the MAE of 4.2461 N/cm2, and R of 0.9880. Conversely, cosine amplitude sensitivity analysis demonstrated that the fine content and dry unit weight influence the prediction of virgin UCS of fine-grained soils.

The Po valley in Northern Italy has been identified as a hot spot area in Europe where pollutant levels are expected to remain problematic in the years to come despite the application of the legislation devoted to air pollution control. High anthropogenic emissions in combination with frequently occurring stagnant atmospheric conditions cause very high PM (particulate matter) concentrations in winter. The POMI project (PO valley Model Inter-comparison exercise) has been set-up to evaluate the capabilities of current air quality models to reproduce the concentration levels reached in this area. One of the outcomes of the project has been the confirmation that air quality models do under-estimate quite significantly the observed PM concentrations in this region. A series of sensitivity test have been run in order to understand the reasons for this underestimation and in particular to highlight the impact of meteorology on modeled concentrations. Different nudging techniques of observations from regulatory networks into MM5 (meteorlogical model) are explored in an attempt to improve the simulation of frequent low wind regimes in the Po valley. The sensitivity of CHIMERE (chemical transport model) is evaluated for January 2005, a period with high PM concentrations. Strengths and weaknesses of the approaches on simulating PM in one of the most polluted and complex areas in Europe are discussed.

A European aerosol phenomenology -4: Harmonized concentrations of carbonaceous aerosol at 10 regional background sites across Europe