Assessment Of Air Pollution Research Articles

The last decade of air pollution epidemiology and the challenges of quantitative risk assessment.

Epidemiologic research and quantitative risk assessment play a crucial role in transferring fundamental scientific knowledge to policymakers so they can take action to reduce the burden of ambient air pollution. This commentary addresses several challenges in quantitative risk assessment of air pollution that require close attention. The background to this discussion provides a summary of and conclusions from the epidemiological evidence on ambient air pollution and health outcomes accumulated since the 1990s. We focus on identifying relevant exposure-health outcome pairs, the associated concentration-response functions to be applied in a risk assessment, and several caveats in their application. We propose a structured and comprehensive framework for assessing the evidence levels associated with each exposure-health outcome pair within a health impact assessment context. Specific issues regarding the use of global or regional concentration-response functions, their shape, and the range of applicability are discussed.

Using EEA data to calculate health impacts of air pollution via the AirQ+ model: a practical example

Abstract The AirQ+ software tool for health risk assessment of air pollution, developed by the World Health Organization, helps quantify the effects of exposure to air pollution. Specifically, AirQ+ can estimate the effects of short-term changes in air pollution (based on risk estimates from time-series studies) and the effects of long-term exposures (using life-tables approach and based on risk estimates from cohort studies). AirQ+ is available in English, French, German and Russian. In this presentation, AirQ+ will be loaded with air pollution data from the European Environment Agency datasets for a quick evaluation of health effects from air pollution in an European city.

Air Pollution Assessment of Thermal Power and Cement Plants in Ninh Binh Province

Cement production and thermal power generation are two primary industries in Ninh Binh province, but they are also among the sources of emissions that can negatively affect to local and regional air quality. This study employs the AERMOD air dispersion modeling system to assess the air pollution levels caused by the operations of the cement and thermal power plants in Ninh Binh province. The results show that the highest average hourly concentrations of TSP, SO2, and NOx are 30.1 µg/m3, 73.2 µg/m3, and 361 µg/m3, respectively; the highest average daily concentrations are 3.77 µg/m3, 2.17 µg/m3, and 49.0 µg/m3, respectively. The predominant direction of pollutant dispersion is towards the West and Southwest, affecting areas within Ninh Binh City and Tam Diep City. The AERMOD model results indicate that pollutant concentrations are lower than the relevant national technical standards. However, it is concerning that a small region within Cuc Phuong National Park shows signs of being affected by these air pollutants. Therefore, stringent control measures on emissions from thermal power and cement plants must be in place to ensure the local air quality and public health.

Evaluation of Ecological Service Functions of Urban Greening Tree Species in Northern China Based on the Species-Specific Air Purification Index

Urban forests, as an integral part of nature-based solutions (NBS), are significant contributors to improving urban air quality, delivering ecological service functions and environmental benefits to human health and well-being. Suitable urban forest management, including proper species selection, needs to be defined to efficiently reduce air pollutants in cities, with a focus on the removal ability of the main air pollutants (PM2.5, PM10, O3, and NO2), the ecological adaptability to O3 and NO2, and allergenic effects. This study ranked 73 urban greening tree species in northern Chinese cities based on their ability to maximize air quality and minimize disservices. This study proposed a novel Species-Specific Air Purification Index (S-API), which is suitable for air quality improvement for tree/shrub species. Urban managers are recommended to select species with an S-API > 1.47—that is, species that have a high removal capacity of PM2.5, PM10, O3, and NO2, are O3- and NO2-tolerant, and are non-allergenic (e.g., Castanea mollissima Blume, Ginkgo biloba L., Hibiscus syriacus L., Ilex chinensis Sims, Juniperus procumbens (Endl.) Iwata et Kusaka, Liriodendron chinense (Hemsl.) Sarg., Morus alba L., Styphnolobium japonicum (L.) Schott, Syringa oblata Lindl., and Ulmus pumila L.). The S-API of urban greening species thus represents a potentially useful metric for air pollutant risk assessment and for selecting appropriate species for urban greening in cities facing serious air pollution challenges.

Assessment of indoor air pollution in causing respiratory illness among women residing in rural areas of Mysuru

Introduction: The use of inefficient and harmful fuels and technology within and outside the home is the primary cause of indoor air pollution. Approximately 2.4 billion people on the planet still cook over open flames and inefficient stoves using solid fuels and kerosene. The availability of cleaner cooking options varies more in rural than in metropolitan settings. Therefore, there is a need to determine the prevalence of indoor air pollution among rural women, its association with respiratory disorders, and the frequency of respiratory illnesses among rural women residing in houses with or without indoor air pollution. Materials and methods: This analytical cross-sectional study was done for a period of six months from June to November 2020. The study conducted in 210 households living in the rural field practice areas of JSS Medical College, Mysuru. For the selection of villages, a multistage random sampling technique was used. Three villages are selected from the rural setting: Suttur, Hadinaru, and Kadakola. The households were included by probability proportionate to the size sampling technique. Pre-tested, semi-structured, phase and content validated questionnaires were used to obtain the details on socio-demographic characteristics, meter values of indoor air pollutants like Particulate Matters (PM1, PM2.5, PM10) and Volatile Organic Compounds (VOCs), respectively with the factors affecting respiratory illness and the episodes of the illness. Data was entered in Microsoft Excel and analyzed using descriptive and inferential statistics using SPSS V.26. Results: The prevalence of indoor air pollution in the present study was 13.3%. In the homes of rural women, there was a statistically significant variation in the concentration of the three particulate matter sources that cause indoor air pollution. Additionally, among the women living in rural families, there was a statistically significant difference in the concentration of PM10 that caused respiratory disease. Conclusion: The current study demonstrated the higher concentrations of pollutants such as PM10, PM2.5, and PM1.0, as well as their significant relation with indoor air pollution in rural families. To prevent indoor air pollution and its health risks, clean energy must be used for household needs.

A Deep Learning Framework for Satellite-Derived Surface PM2.5 Estimation: Enhancing Spatial Analysis in the United States

Abstract Monitoring fine particulate matter (PM2.5) is crucial for evaluating air quality and its effects on public health. However, the limited distribution of monitoring stations presents a challenge in accurately assessing air pollution, especially in areas distant from these stations. To address this challenge, our study introduces a two-step deep learning approach for estimating daily gap-free surface PM2.5 concentrations across the contiguous United States (CONUS) from 2018 to 2022, with a spatial resolution of 4 km. In the first phase, we employ a Depthwise-Partial Convolutional Neural Network (DW-PCNN) to fill gaps between surface PM2.5 stations, utilizing Aerosol Optical Depth (AOD) data from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the second phase, we integrate the PM2.5 grids imputed by the DW-PCNN with meteorological and anthropogenic variables into a Deep Convolutional Neural Network (Deep-CNN) to further enhance the accuracy of our estimation. This enables us to estimate gap-free surface PM2.5 concentrations accurately, evidenced by a Pearson’s correlation coefficient (R) of 0.92 and an Index of Agreement (IOA) of 0.96 in ten-fold cross-validation. We also introduce a grid-based method for calculating PM2.5 Design Values (DV), providing a continuous spatial representation of PM2.5 DV that enhances the traditional station-based approach. Our grid-based DV representations offer a comprehensive perspective on air quality, facilitating more detailed analysis. Furthermore, our model's ability to provide spatiotemporally consistent, gap-free PM2.5 data addresses the issue of missing values, supporting health impact research, policy formulation, and the accuracy of environmental assessments.

A prospective study on the cardiorespiratory effects of air pollution among residents of the Tibetan Plateau

The Tibetan Plateau is characterized by high ozone concentration which poses a significant public health concern. However, the causal evidence linking ozone pollution to adverse cardiopulmonary health impacts, as well as the understanding of its underlying biological mechanisms, remains limited. Additionally, exposure levels to particulate and other gaseous air pollutants along with their associated health effects in this region are largely unknown. To address these gaps, we conducted a prospective follow-up study in Tibet from May 2021 to November 2021. In consideration of the potential synergistic effects of chronic hypobaric hypoxia, two Tibetan cities with different altitudes, Lhasa (3650 m) and Nyingchi (3000 m), were chosen to implement atmospheric monitoring and health measurement. We employed cutting-edge, high-precision instruments at stationary monitoring sites to measure ambient air pollution and collected particle samples. Portable devices were used to monitor personal exposure levels of ozone and black carbon. A total of 212 healthy Tibetan college students participated in up to four clinical visits, yielding 774 visits in total, during which functional endpoints were measured and biological samples were collected. The primary aim of this study is to evaluate the cardiorespiratory effects of ambient ozone under hypoxic conditions, where its impact may be amplified due to the region's unique environmental characteristics. The secondary aim is to provide a comprehensive assessment of other air pollutants, including their exposure levels, sources, and health effects. By addressing these aims, the study offers valuable insights into air quality and its health implications in this unique high-altitude setting. This paper outlines the research motivation, measurement framework, and preliminary findings.

Impact of net zero policy scenarios on air pollution inequalities in England and Wales

BackgroundThe UK is committed to achieve net zero greenhouse gas emissions by 2050. The suite of policies needed to reach net zero will lead to improvements in air quality and, consequently, could lessen air pollution inequalities. We assessed air pollution inequalities across different sociodemographic groups in England and Wales and explored how these might be differentially impacted by future air pollution projections in 2030 and 2040 under net zero policies. MethodsWe employed a geodemographic classification approach to categorise neighbourhoods into five distinct clusters based on 2021 UK Census sociodemographic variables. We modelled fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations for the year 2019, and predicted concentrations in 2030 and 2040. We compared a business-as-usual (BAU) scenario and two policy pathways to achieve net zero currently considered by the UK government. We aggregated air pollution concentrations to the neighbourhood level and assessed differential neighbourhood-level concentrations across the geodemographic groups using descriptive statistics and box plots. ResultsThe Urban Central Professionals group experienced 14 µg/m3 higher average NO2 concentrations compared with the Rural Elderly group in 2019. Despite substantial improvements to air quality in 2030 and 2040 of up to 6.3 µg/m3 for NO2 based on BAU, and further reductions of up to 2.4 µg/m3 NO2 under net zero policies, the overall pattern of inequality persists, but is predicted to be less pronounced. ConclusionsOur findings demonstrate the effectiveness of targeted policies and innovations in reducing both air quality and greenhouse gas emissions and in bridging the environmental inequality gap. Our findings are essential to develop targeted communication campaigns to secure acceptance and willingness across the sociodemographic spectrum to support the significant behavioural changes needed to achieve net zero, by highlighting the wider co-benefits to the environment and health of such policies.

Salix humboldtiana as an Indicator of Air Pollution by Trace Metals in the Urban Areas of the City of Loja, Southern Ecuador

Air pollution is the most important environmental problem in urban areas related to vehicular traffic and industrial activities. The widespread presence of common urban trees, such as Salix humboldtiana, and their ability to tolerate diverse environmental conditions make this species an especially promising candidate for assessing environmental metal contamination. Therefore, biomonitoring with vascular plants has been widely used to assess air pollution, especially the accumulation of trace metal concentrations. Therefore, for the first time, we analyzed the concentration of trace metals using Salix humboldtiana in twelve areas with different levels of pollution in a city in Southern Ecuador. For this purpose, samples were taken from each site to assess the accumulation of trace metals such as Zn, Mn, Fe, Cd, Cr, Pb, Cu, Al, and Ni. The results obtained showed significant differences in the concentrations of Zn, Mn, Fe, and Cd between the urban areas and the control area, indicating that the central areas were the most polluted by vehicular traffic. However, these findings suggest that Salix humboldtiana may not be a particularly effective tool for quantifying levels of environmental metal contamination such as Cu and Ni, at least in urban areas in the city of Loja. This study has demonstrated that Salix humboldtiana leaves can effectively monitor trace metals associated with road traffic emissions in areas with varying levels of vehicular activity, indicating that vascular plants can be utilized for this purpose in tropical cities.

Advances and future directions of environmental risk research: A bibliometric review

Environmental risk is one of the world’s most significant threats, projected to be the leading risk over the next decade. It has garnered global attention due to increasingly severe environmental issues, such as climate change and ecosystem degradation. Research and technology on environmental risks are gradually developing, and the scope of environmental risk study is also expanding. Here, we developed a tailored bibliometric method, incorporating co-occurrence network analysis, cluster analysis, trend factor analysis, patent primary path analysis, and patent map methods, to explore the status, hotspots, and trends of environment risk research over the past three decades. According to the bibliometric results, the publications and patents related to environmental risk have reached explosive growth since 2018. The primary topics in environmental risk research mainly involve (a) ecotoxicology risk of emerging contaminants (ECs), (b) environmental risk induced by climate change, (c) air pollution and health risk assessment, (d) soil contamination and risk prevention, and (e) environmental risk of heavy metal. Recently, the hotspots of this field have shifted into artificial intelligence (AI) based techniques and environmental risk of climate change and ECs. More research is needed to assess ecological and health risk of ECs, to formulize mitigation and adaptation strategies for climate change risks, and to develop AI-based environmental risk assessment and control technology. This study provides the first comprehensive overview of recent advances in environmental risk research, suggesting future research directions based on current understanding and limitations.

In Situ and Rapid Toxicity Assessment of Air Pollution by Self-Assembly Passive Colonization Hydrogel.

Air pollution is a leading environmental health risk factor, and in situ toxicity assessment is urgently needed. Bacteria-based bioassays offer cost-effective and rapid toxicity assessments. However, the application of these bioassays for air toxicity assessment has been challenging, due to the instability of bacterial survival and functionality when directly exposed to air pollutants. Here, we developed an approach employing self-assembly passive colonization hydrogel (SAPCH) for in situ air toxicity assessment. The SAPCH features a core-shell structure, enabling the quantitatively immobilization of bacteria on its shell while continuously provides nutrients from its core. An antimicrobial polyelectrolyte layer between the core and shell confines bacteria to the air-liquid interface, synchronizing bacterial survival with exposure to air pollutants. The SAPCH immobilized a battery of natural and recombinant luminescent bacteria, enabling simultaneous detection of various toxicological endpoints (cytotoxicity, genotoxicity and oxidative stress) of air pollutants within 2 h. Its sensitivity was 3-5 orders of magnitude greater than that of traditional liquid-phase toxicity testing, and successfully evaluating the toxicity of volatile organic compounds and combustion smoke. This study presents a method for in situ, rapid, and economical toxicity assessment of air pollution, making a significant contribution to future air quality monitoring and control.

Comparative Assessment of the Impact of COVID-19 Lockdown on Air Quality: A Multinational Study of SARS-CoV-2 Hotspots.

In response to the global COVID-19 pandemic, nations implemented lockdown measures to contain the virus. This study assessed air pollution levels during and after lockdowns, focusing on the following heavily affected locations: Oulu and Helsinki in Finland, Paris in France, Madrid in Spain, Milan in Italy, and Wuhan in China. Air Quality Index (AQI) data from these locations over two years were analyzed to understand the effects of lockdowns. The study compared COVID-19 lockdowns in these six cities with SARS-CoV-2 measurements using statistical methods. Variations in outdoor pollutants were evaluated through tests, revealing significant differences. Parametric analyses and regression were employed to study the impacts of lockdown measures on pollution and their relationships. The study comprehensively analyzed the effects of COVID-19 lockdowns on air quality, identifying differences, quantifying changes, and exploring patterns in each city. Pollutant correlations varied among cities during the lockdowns. Regression analysis highlighted the impact of independent variables on pollutants. Decreases in NO2 were observed in Helsinki, Madrid, Oulu, Paris, and Milan, reflecting reduced traffic and industrial activities. Reductions in PM2.5 and PM10 were noted in these cities and in Wuhan, except for O3 levels, which increased. The reduction in human activities improved air quality, particularly for NO2 and PM10. Regional variations underscore the need for tailored interventions. The study observed a substantial decrease in both PM2.5 and NO2 levels during the COVID-19 lockdowns, indicating a direct correlation between reduced human activities, such as transportation and industrial operations, and improved air quality. This underscores the potential impact of environmental measures and suggests the need for sustainable practices to mitigate urban pollution.

Contribution to the study of possible transport of PM10 aerosols in the eastern part of the Czech Republic

The article deals with the assessment of air pollution in the eastern regions of the Czech Republic: Moravia and Silesia, where the limits for the protection of human health for PM and benzo[a]pyrene are significantly exceeded, especially in the north-eastern area. The extent to which this heavily polluted part of Moravia and Silesia affects the central, southern and south-eastern parts of Moravia, i.e. south-eastern part of the Czech Republic, has often been discussed but not proven. The assessment demonstrated the influence and extent of pollutant transport by determining the prevailing daily wind direction. The prevailing NE wind direction results in worse dispersion conditions and higher PM10 concentrations throughout the study area. Conversely, the SW wind direction is a carrier of better dispersion conditions with lower PM10 concentrations in the area. The effect of transport of PM10 pollution in the daily type from the NE direction can be observed at those sites located in the NE of the area of interest from the border with Poland to the Prerov site. In the case of more southerly sites, the methodology used does not allow to determine whether and to what extent they are affected by pollution transport from the northeastern part of Moravia or from neighbouring Poland. This methodology is particularly useful when only 24-h pollutant concentrations are available (not at more detailed, e.g. 1-hour intervals). The internal methodology of the Czech Hydrometeorological Institutefor calculating back trajectories of wind from ground stations is particularly applicable for detecting the origin of short-term episodes with high pollutant concentrations.

Assessment of Air Pollution Levels from a Building Construction Site on Lagos Island

The introduction highlights the challenges of air pollution from construction activities on a site in Lagos Island, Nigeria, emphasizing the need for comprehensive studies to assess air pollution levels and evaluate its implications for public health and environmental quality. The methodology outlines the monthly data collection process, using the Earth Sense Zephyr (equipped with electrochemical detectors for gases) to measure CO, NO, NO₂, O₃, and Optical light scattering for particles) to measure PM₂.₅, and PM₁₀, and the ARA n-FRM Sampler for additional data collection on PM₂.₅, and PM₁₀. The study found that CO, NO, and NO₂ levels were influenced by construction activities, vehicle emissions and industrial sources, with notable peaks in CO and NO concentrations during specific months. Ozone levels remained consistently low, likely due to the "titration effect," while particulate matter (PM₂.₅ and PM₁₀) showed significant seasonal variation, peaking during the dry season due to construction dust and dry weather conditions. The findings underscore the need for stringent regulatory measures and effective dust control practices, particularly during periods of increased construction activity and dry weather, to mitigate air pollution and protect public health. In conclusion, the study provides valuable insights into the dynamics of air pollution from a typical construction site in Lagos Island, emphasizing the urgency of sustainable interventions to safeguard public health and environmental integrity. The study proposes enhanced monitoring and surveillance, stringent regulatory measures, promotion of sustainable construction practices, and public awareness and education, to address the challenges associated with construction-related air pollution on Lagos Island.

A novel approach for large-scale characterization of residential cooking-generated PM with computer vision and low-cost sensors

Cooking is one of the major sources of indoor particulate matter (PM), which poses significant health risks and is a severe health hazard. Current studies lack an economical and effective analytical framework for quantifying inhalable particles (PM10) and fine particulate matter (PM2.5) from residential cooking activities on a large scale under real-world scenarios. This study bridges this gap by employing computer vision (CV) technology and readily available sensors. We collected data over a month in real-world settings, including cooking videos and air quality data (indoor PM10, PM2.5, CO2, temperature, relative humidity, and outdoor PM10 and PM2.5 concentrations). To classify high-emission (pan-frying, stir-frying, deep-frying) and low-emission (stewing, steaming, boiling, non-cooking) activities, we developed and validated a robust CV model named “Cooking-I3D.” This model leverages a pre-trained Two-Stream Inflated 3D ConvNet (I3D) architecture. We then assessed the efficacy of the CV-predicted cooking method in PM characterization using a first-order multivariate autoregressive model, controlling for environmental factors. The Cooking-I3D model achieved exceptional performance, boasting an accuracy of 95 % and an Area Under the Curve (AUC) of 0.98. Our results indicate that a single 6-minute high-emission cooking event triggers a 21–25 % increase in indoor PM concentrations and a 23–24 % increase in the indoor/outdoor ratio, with relative errors in these estimates ranging from 10 to 21 %. This innovative method offers a powerful tool for long-term assessment of cooking-related indoor air pollution and facilitates precision exposure assessment in human health studies.

Health impact assessment of air pollution in the Metropolitan Area of Costa Rica using AirQ+.

Health impact assessment of air pollution in the Metropolitan Area of Costa Rica using AirQ+.

Increasing Capacity to Assess Air Pollution Disparities in Atlantic Canada

Increasing Capacity to Assess Air Pollution Disparities in Atlantic Canada

Optimizing BenMAP health impact assessment with meteorological factor driven machine learning models

This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model's accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.

Assessment of ambient air quality of Lucknow city post monsoon 2023

This study monitoring and assessment of air pollutants, including lead and nickel trace metals, gases such as sulphur dioxide (SO2) and nitrogen dioxide (NO2), and respirable suspended particulate matter (PM10) and fine particulate matter (PM2.5), were done at nine locations that were divided into three categories: residential, commercial, and industrial of the ambient air quality in the city of the post-monsoon. With an average of 148.9 µg m-3, the 24-hour concentrations of PM10 ranged from 58.5 to 337.8 µg m-3, whereas the 24-hour concentrations of PM2.5 ranged from 32.5 to 118.4 µg m-3, with an average of 70.5 µg m-3. At every location, the mean concentrations of PM10 exceeded the allowable threshold of 100 µg m-3. While PM2.5 levels, with the exception of Aliganj, Vikas Nagar, and Chowk, exceeded the 60µg m-3 national limit. Pb and Ni, which are PM10 trace elements, range from 0.0028 to 0.0370 µg m-3, with an average of 0.0147 µg m-3 for Pb and 1.30 to 11.75 µg m-3, with an average of 6.62 µg m-3 for Ni. During a 24-hour period, the levels of SO2 varied between 7.9 and 57.7 µg m-3, with an average of 20.8 µg m-3. Conversely, the levels of NO2 varied between 9.3 and 89.0 µg m-3, with an average of 37.8. Between µg m-3 the post-monsoon of 2022 and 2023, SO2 concentrations declined by 16.0% and NO2 values increased by 22.0%, respectively. On the other hand, average SO2 and NO2 levels were significantly lower than the 80µg m-3permitted limits set by the CPCB (NAAQS-2009).

Assessing and monitoring air quality in cities and urban areas with a portable, modular and low-cost sensor station: calibration challenges

ABSTRACT Air pollution affects not only the air in cities but also extends to all indoor environments (homes, offices, schools, public places, transportation, etc.), where we spend between 80% and 90% of our time. Both indoor and outdoor air quality have emerged as significant health concerns and are integral to national strategies implemented by health and environmental institutes in each country. Recently, complaints regarding outdoor air quality have risen in cities, primarily due to automobile traffic and industrial activities in urban areas, and also indoors within homes, offices, and schools. The following paper presents a methodology for the calibration of low-cost monitoring stations based on measurements in a couple of cities in Colombia as part of the development of a project to reduce the environmental awareness gap in urban areas for the estimation of the air quality through low-cost, flexible, modular, and mobile air quality monitoring station design that could be used to assess air pollution in different indoor and outdoor environments. With the implementation of the low-cost stations, we have calibrated and evaluated the performance of the stations using usual linear regression methods, but we have also explored the use of unsupervised estimation with the help of machine learning algorithms, specifically with Random Forest estimators. We have found a significant improvement with using Random Forest for station calibration compared with those found using simple linear regressions for calibration effects. We have found that all the models offer a significant improvement in terms of RMSE. The regression model improves RMSE by up to 70%, while the multiple regression model does so by up to 73%. However, it is the Random Forest that shows the most remarkable improvement, with a reduction in RMSE of up to 86%.