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Dual concerns involving the rise in airborne pollutant levels and bulging need to protect-preserve human health have propelled the search for innovative means for air quality monitoring to aid in evidence-based decision-making (pollution prevention-mitigation). In this regard, moss bags have gathered a great deal of attention as active biomonitors. In this reflective discourse, we systematically review the world literature to present a bird’s eye view of moss bag applications and advances while highlighting potential concerns. We begin with a brief note on mosses as biomonitors, highlighting the advantages of moss bags over the passive technique (native moss), other living organisms (lichens, vascular plants), and instrument-based measurements. A major strand of moss bag research involves urban ecosystem sustainability studies (e.g., street tunnels and canyons, parks), while others include event-specific monitoring and change detection (e.g., SARS-CoV-2 Lockdown), indoor-outdoor air quality assessment, and change detection in land use patterns. Recent advances include biomagnetic studies, radioisotopic investigations, and mobile applications. Efforts are currently underway to couple moss bag results with a suite of indicators [e.g., relative accumulation factor (RAF), contamination factor (CF), pollution load index (PLI), enrichment factor (EF)] and spatially map the results for holistic appraisal of environmental quality (hot spot detection). However, while moss bag innovations and applications continue to grow over time, we point to fundamental concerns/uncertainties (e.g., lack of concordance in operational procedures and parameterization, ideal species selection, moss vitality) that still need to be addressed by targeted case studies, before the moss results could be considered in regulatory interventions.

Air pollution produces major environmental health problems with a vast number of entropies that can affect healthy, sustainable environments across the globe. Millions of people are dying prematurely each year as a direct cause of poor air quality. According to recent studies, living within 50 meters of any significant road can increase the risk of lung cancer by up to 10%. World Health Organization declares that approximately 3.7 million people died worldwide in 2012 due to outdoor air pollution. In this analysis, we analyzed air pollutants that were released into the air from a wide range of sources, such as motor vehicles, industrial combustion processes, etc. We analyzed the Sentinel-5 precursor data, which provides time series data on a multitude of trace gaseous compounds such as CO, NO2, SO2, O3, PM10, PM2.5 aerosols, etc. with efficient statistics and special resolution. For better comparison, we have trained our statistical atmospheric data with deep learning methodology and analyzed them to obtain a reference for air quality in India. This study describes the scientific aspects and probable atmospheric composition entropy due to pollution. We also presented the overall operational product outcomes and emissions from the energy sectors, which involves the advancement of data analysis in a particular coordinate system.

In light of arguments that citizen science has the potential to make environmental knowledge and policy more robust and democratic, this article inquires into the factors that shape the ability of citizen science to actually influence scientists and decision makers. Using the case of community-based air toxics monitoring with \"buckets,\" it argues that citizen science's effectiveness is significantly influenced by standards and standardized practices. It demonstrates that, on one hand, standards serve a boundary-bridging function that affords bucket monitoring data a crucial measure of legitimacy among experts. On the other hand, standards simultaneously serve a boundary-policing function, allowing experts to dismiss bucket data as irrelevant to the central project of air quality assessment. The article thus calls attention to standard setting as an important site of intervention for citizen science-based efforts to democratize science and policy.

Efficient air quality management is critical to protect public health from the adverse impacts of air pollution. To evaluate the effectiveness of air pollution control strategies, the US Environmental Protection Agency （US EPA） has developed the Software for Model Attainment Test-Community Edition （SMAT-CE） to assess the air quality attainment of emission reductions, and the Environmental Benefits Mapping and Analysis Program- Community Edition （BenMAP-CE） to evaluate the health and economic benefits of air quality improvement respectively. Since scientific decision-making requires timely and coherent information, developing the linkage between SMAT-CE and BenMAP-CE into an integrated assessment platform is desirable. To address this need, a new module linking SMAT-CE to BenMAP-CE has been developed and tested. The new module streamlines the assessment of air quality and human health benefits for a proposed air pollution control strategy. It also implements an optimized data gridding algorithm which significantly enhances the computational efficiency without compro- mising accuracy. The performance of the integrated software package is demonstrated through a case study that evaluates the air quality and associated economic benefits of a national-level control strategy of PM2.5. The results of the case study show that the proposed emission reduction reduces the number of nonattainment sites from 379 to 25 based on the US National Ambient Air Quality Standards, leading to more than USS334billion ofeconomic benefits annually from improved public health. The integration of the science-based software tools in this study enhances the efficiency of developing effective and optimized emission control strategies for policy makers.

Urban air pollution from potentially toxic elements (PTEs) presents a critical threat to public health and environmental sustainability. The current study employed Syntrichia moss in a passive biomonitoring capacity to ascertain the levels of atmospheric PTE pollution in Thessaloniki, Greece. A comprehensive collection of 192 moss samples was undertaken at 16 urban sampling points over the March–July 2024 period. Concentrations of 21 PTEs were quantified using ICP-MS, and contamination levels were assessed through contamination factor (CF), enrichment factor (EF), and pollution load index (PLI). Positive matrix factorization (PMF) modeling and multivariate statistical analyses were used to identify pollution sources and spatiotemporal variations. Results revealed persistent hotspots with significant anthropogenic enrichments of elements, such as Fe, Mn, Sn in industrial zones and Tl, Ce, Pt in traffic corridors. PMF modeling attributed 48% of the measured PTE variance to traffic-related sources, 35% to industrial sources, and 17% to crustal material. Seasonal transitions showed a significant 3.5-fold increase in Tl during summer, indicating elevated traffic-related emissions. This integrated multi-index and source apportionment framework demonstrates the efficacy of Syntrichia moss for high-resolution urban air quality assessment. The approach offers a cost-effective, scalable, and environmentally friendly tool to support EU-aligned air quality management strategies.

Surging incidents of air quality-related public health hazards, and environmental degradation, have prompted the global authorities to seek newer avenues of air quality monitoring, especially in developing economies, where the situation appears most alarming besides difficulties around ‘adequate’ deployment of air quality sensors. In the present narrative, we adopt a systematic review methodology (PRISMA, Preferred Reporting Items for Systematic reviews and Meta-Analyses ) around recent global literature (2002–2022), around moss-based passive biomonitoring approaches which might offer the regulatory authorities a complementary means to fill ‘gaps’ in existing air quality records. Following the 4-phased search procedure under PRISMA, total of 123 documents were selected for review. A wealth of research demonstrates how passive biomonitoring, with strategic use of mosses, could become an invaluable regulatory (and research) tool to monitor atmospheric deposition patterns and help identifying the main drivers of air quality changes (e.g., anthropogenic and/or natural). Besides individual studies, we briefly reflect on the European Moss Survey, underway since 1990, which aptly showcases mosses as ‘naturally occurring’ sensors of ambient air quality for a slew of metals (heavy and trace) and persistent organic pollutants, and help assessing spatio-temporal changes therein. To that end, we urge the global research community to conduct targeted research around various pollutant uptake mechanisms by mosses (e.g., species-specific interactions, environmental conditions, land management practices). Of late, mosses have found various environmental applications as well, such as in epidemiological investigations, identification of pollutant sources and transport mechanisms, assessment of air quality in diverse and complex urban ecosystems, and even detecting short-term changes in ambient air quality (e.g., COVID-19 Lockdown), each being critical for the authorities to develop informed and strategic regulatory measures. To that end, we review current literature and highlight to the regulatory authorities how to extend moss-based observations, by integrating them with a wide range of ecological indicators to assess regional environmental vulnerability/risk due to degrading air quality. Overall, an underlying motive behind this narrative was to broaden the current regulatory outlook and purview, to bolster and diversify existing air quality monitoring initiatives, by coupling the moss-based outputs with the traditional, sensor-based datasets, and attain improved spatial representation. However, we also make a strong case of conducting more targeted research to fill in the ‘gaps’ in our current understanding of moss-based passive biomonitoring details, with increased case studies.

The aim of this study was to assess air quality by using medium-cost sensors in recreational areas that are not covered by permanent monitoring. Concentrations of air pollutants PM2.5, PM10, PM1, CO, O 3 , NO 2 in the Niedzica recreational area in southern Poland were obtained. The research revealed that in cold weather, particulate matter concentrations significantly exceeded acceptable levels determined for PM2.5 and PM10. The most important factor that affects air quality within the studied area seems to be the combustion of poor quality fuels for heating purposes. The information obtained by the research presented could be a useful tool for local authorities to make environmental decisions, based on the potential health impacts of poor air quality levels on the population.

Physical characterisation of PM 2.5 , PM 10 and surface ozone measured during the period from 17 July to 21 August 2012 at four strategic locations in and around the Lumbini Protected Zone, Nepal, is done to assess air quality of the region and understand qualitatively source mechanisms of these pollutants. The measurement locations are Panditarama Lumbini International Vipassana Meditation Centre, Parsahawa, Bhairahawa and Tilaurakot, representing monastic, industrial, urban and control areas, respectively. The overall average concentration of PM 2.5 at these locations is ∼ 19 ± 12 , 35 ± 13 , 35 ± 11 and 25 ± 6 μ g/m 3 and of PM 10 is ∼ 25 ± 11 , 103 ± 41 , 58 ± 15 and 32 ± 7 μ g/m 3 , respectively. PM 2.5 never crosses the safe limit of the National Ambient Air Quality Standards of Nepal (NNAAQS) in the monastic and control areas but either crosses the NNAAQS occasionally or remains in its vicinity at the other two locations. The PM 10 concentration frequently exceeds the safe limit in the industrial area but not in the other remaining areas. The analysis indicates the dominance of the impact of local sources and boundary layer thickness on the atmospheric loadings of the particulate matter. The daily average mixing ratio of surface ozone remains normally low at all the four observational sites although the mixing ratio of ozone at Panditarama Lumbini International Vipassana Meditation Centre is much lower than the NNAAQS but higher than that observed at Tilaurakot.

The Copernicus Atmosphere Monitoring Service (CAMS), part of the European Union’s Earth observation program Copernicus, entered operations in July 2015. Implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF) as a truly European effort with over 23,500 direct data users and well over 200 million end users worldwide as of March 2022, CAMS delivers numerous global and regional information products about air quality, inventory-based emissions and observation-based surface fluxes of greenhouse gases and from biomass burning, solar energy, ozone and UV radiation, and climate forcings. Access to CAMS products is open and free of charge via the Atmosphere Data Store. The CAMS global atmospheric composition analyses, forecasts, and reanalyses build on ECMWF’s Integrated Forecasting System (IFS) and exploit over 90 different satellite data streams. The global products are complemented by coherent higher-resolution regional air quality products over Europe derived from multisystem analyses and forecasts. CAMS information products also include policy support such as quantitative impact assessment of short- and long-term pollutant-emission mitigation scenarios, source apportionment information, and annual European air quality assessment reports. Relevant CAMS products are cited and used for instance in IPCC Assessment Reports. Providing dedicated support for users operating smartphone applications, websites, or TV bulletins in Europe and worldwide is also integral to the service. This paper presents key achievements of the CAMS initial phase (2014–21) and outlines some of its new components for the second phase (2021–28), e.g., the new Copernicus anthropogenic CO₂ emissions Monitoring and Verification Support capacity that will monitor global anthropogenic emissions of key greenhouse gases.

The PurpleAir PA-II unit is a low-cost sensor for monitoring changes in the concentrations of particulate matter (PM) of various sizes. There are currently more than 10 000 PA-II units in use worldwide; some of the units are located in areas where no other reference air monitoring system is present. Previous studies have examined the performance of these PA-II units (or the sensors within them) in comparison to a co-located reference air monitoring system. However, because PA-II units are installed by PurpleAir customers, most of the PA-II units are not co-located with a reference air monitoring system and, in many cases, are not near one. This study aims to examine how each PA-II unit performs under atmospheric conditions when exposed to a variety of pollutants and PM2.5 concentrations (PM with an aerodynamic diameter smaller than 2.5 µm), when at a distance from the reference sensor. We examine how PA-II units perform in comparison to other PA-II units and Environmental Protection Agency (EPA) Air Quality Monitoring Stations (AQMSs) that are not co-located with them. For this study, we selected four different regions, each containing multiple PA-II units (minimum of seven per region). In addition, each region needed to have at least one AQMS unit that was co-located with at least one PA-II unit, all units needed to be at a distance of up to 5 km from an AQMS unit and up to 10 km between each other. Correction of PM2.5 values of the co-located PA-II units was implemented by multivariate linear regression (MLR), taking into account changes of temperature and relative humidity. The fit coefficients, received from the MLR, were then used to correct the PM2.5 values in all the remaining PA-II units in the region. Hourly PM2.5 measurements from each PA-II unit were compared to those from the AQMSs and other PA-II units in its region. The correction of the PM2.5 values improved the R-squared value (R2), root-mean-square error (RMSE), and mean absolute error (MAE) and slope values between all units. In most cases, the AQMSs and the PA-II units were found to be in good agreement (75 % of the comparisons had a R2>0.8); they measured similar values and followed similar trends; that is, when the PM2.5 values measured by the AQMSs increased or decreased, so did those of the PA-II units. In some high-pollution events, the corrected PA-II had slightly higher PM2.5 values compared to those measured by the AQMS. Distance between the units did not impact the comparison between units. Overall, the PA-II unit, after corrections of PM2.5 values, seems to be a promising tool for identifying relative changes in PM2.5 concentration with the potential to complement sparsely distributed monitoring stations and to aid in assessing and minimizing the public exposure to PM.