Monitoring of PM2.5 Concentrations by Learning from Multi-Weather Sensors

Pharmacotherapeutic Strategies for Fine Particulate Matter-Induced Lung and Cardiovascular Damage: Marketed Drugs, Traditional Chinese Medicine, and Biological Agents

Fine particulate matter (PM2.5), defined as airborne particles with a diameter of ≤ 2.5 μm, represents a major constituent of air pollution and has been globally implicated in exacerbating public health burdens by elevating morbidity and mortality rates associated with respiratory and cardiovascular diseases (CVDs). Adverse health effects of PM2.5 exposure manifest across diverse susceptibility profiles and durations of exposure, spanning both acute and chronic timelines. While prior reviews have predominantly focused on elucidating the toxicological mechanisms underlying PM2.5-induced pathologies, there remains a paucity of comprehensive summaries addressing therapeutic interventions for cardiopulmonary damage. This review systematically synthesizes pharmacological agents with potential therapeutic efficacy against PM2.5-induced pulmonary and cardiovascular injury. By integrating mechanistic insights with translational perspectives, this work aims to provide a foundational framework for advancing research into novel therapeutic strategies targeting PM2.5-associated cardiopulmonary disorders.

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods

OBJECTIVE

The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors.

METHOD

Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation.

RESULTS

Indoor particles were measured in a range of 0.8-370.7 μg/m3 and 1.9-465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m3 and 0.0-493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively.

CONCLUSIONS

Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Investigating the Sensitivity of Low-Cost Sensors in Measuring Particle Number Concentrations across Diverse Atmospheric Conditions in Greece and Spain

Low-cost sensors (LCSs) for particulate matter (PM) concentrations have attracted the interest of researchers, supplementing their efforts to quantify PM in higher spatiotemporal resolution. The precision of PM mass concentration measurements from PMS 5003 sensors has been widely documented, though limited information is available regarding their size selectivity and number concentration measurement accuracy. In this work, PMS 5003 sensors, along with a Federal Referral Methods (FRM) sampler (Grimm spectrometer), were deployed across three sites with different atmospheric profiles, an urban (Germanou) and a background (UPat) site in Patras (Greece), and a semi-arid site in Almería (Spain, PSA). The LCSs particle number concentration measurements were investigated for different size bins. Findings for particles with diameter between 0.3 and 10 μm suggest that particle size significantly affected the LCSs' response. The LCSs could accurately detect number concentrations for particles smaller than 1 μm in the urban (R² = 0.9) and background sites (R² = 0.92), while a modest correlation was found with the reference instrument in the semi-arid area (R² = 0.69). However, their performance was rather poor (R² < 0.31) for coarser aerosol fractions at all sites. Moreover, during periods when coarse particles were dominant, i.e., dust events, PMS 5003 sensors were unable to report accurate number distributions (R² values < 0.47) and systematically underestimated particle number concentrations. The results indicate that several questions arise concerning the sensors' capabilities to estimate PM_2.5 and PM₁₀ concentrations, since their size distribution did not agree with the reference instruments.

Statistical Analysis of the Impact of COVID-19 on PM2.5 Concentrations in Downtown Quito during the Lockdowns in 2020

In this paper, a comparative analysis between the PM2.5 concentration in downtown Quito, Ecuador, during the COVID-19 pandemic in 2020 and the previous five years (from 2015 to 2019) was carried out. Here, in order to fill in the missing data and achieve homogeneity, eight datasets were constructed, and 35 different estimates were used together with six interpolation methods to put in the estimated value of the missing data. Additionally, the quality of the estimations was verified by using the sum of squared residuals and the following correlation coefficients: Pearson's r, Kendall's τ, and Spearman's ρ. Next, feature vectors were constructed from the data under study using the wavelet transform, and the differences between feature vectors were studied by using principal component analysis and multidimensional scaling. Finally, a robust method to impute missing data in time series and characterize objects is presented. This method was used to support the hypothesis that there were significant differences between the PM2.5 concentration in downtown Quito in 2020 and 2015-2019.