Found 9985 publications. Showing page 76 of 400:
Citizen-operated low-cost sensors for estimating outdoor particulate matter infiltration
Fine particulates observed indoors exhibit high variability, influenced by both indoor emission sources and the infiltration of outdoor particles through open spaces and the incomplete building insulation. This study examines the relationship between indoor and outdoor PM2.5 levels in Legionowo, Poland, using data from low-cost air quality sensors operated by citizens. The indoor PM2.5 was lower than outdoor levels (median PM2.5: 1.9–17.3 μg m–3 indoors and 6.7–27.9 μg m–3 outdoors), with occasional peaks attributed to potential indoor emission sources. Statistical analysis identified emission events—particularly during cooking and household-heating periods—occurring more frequently from October to April. During this period, nearly 17% of indoor PM2.5 measurements were attributed to indoor emission sources after 18:00 LT, representing a 7% increase compared to the May–September period. In the absence of indoor sources, outdoor particles accounted for 29% to 75% of indoor concentrations, highlighting the significance of infiltration. This research emphasizes how citizen-generated data using low-cost sensors, after post-processing, can provide decision-ready information as for example outdoor particles’ infiltration factors for each building. The knowledge of the infiltration factor enables the determination of the contribution of indoor and outdoor sources to each resident’s exposure to airborne PM. This information can help decision-makers in devising interventions such as prioritizing indoor ventilation, reducing indoor activities resulting in increased exposure, and addressing outdoor pollution sources.
2025
Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.
2023
2014
2017
2015
This paper examines the creation of fine resolution maps at 100 m x 100 m resolution using statistical downscaling for the area of Prague, as a case study. This Czech city was selected due to the fine resolution proxy data available for this city. The reference downscaling methodology used is the linear regression and the interpolation of its residuals by the area-to-point kriging. Next to this, several other methods of statistical downscaling have been also executed. The results of different downscaling methods have been compared mutually and against the data from the monitoring stations of Prague, separately for urban background and traffic areas.
The downscaled maps in 100 m x 100 m resolution have been constructed for the area of Prague for three pollutants, namely for NO2, PM10 and PM2.5. Several methods of the statistical downscaling have been compared mutually and against the data from the monitoring stations. In general, the best results are given by the linear regression and the interpolation of its residuals, either by the area-to-point kriging or the bilinear interpolation. In the maps, one can see overall realistic spatial patterns, the main roads in Prague are visible through higher air pollution levels. This is distinct especially for NO2, while for PM10 and PM2.5 the differences between road increments and urban background are smaller as would be expected. The results of the case study for Prague have proven the usefulness of the statistical downscaling for the air quality mapping, especially for NO2. In addition, the population exposure estimates based on the downscaled mapping results have been also calculated.
ETC/HE
2023
2007
The ClairCity Horizon2020 project aims to contribute to citizen-inclusive air quality and carbon policy making in middle-sized European cities. It does so by investigating citizens’ current behaviours as well as their preferred future behaviours and policy measures in six European cities1 through an extensive citizen and stakeholder engagement process. The project also models the possible future impacts of citizens’ policy preferences and examines implementation possibilities for these measures in the light of the existing institutional contexts in each city (Figure 0-1). This report summarises the main policy results for Amsterdam (the Netherlands).
ClairCity Project
2020
ClairCity aims to contribute to citizen-inclusive air quality and carbon policy making in middle-sized European cities. It does so by investigating citizens’ current behaviours, their preferred future behaviours and their preferred future policy measures in six European cities. The project also examines the possible future impacts of citizens’ policy preferences and implementation possibilities for these measures in the light of the existing institutional contexts in each city. With this aim, ClairCity has carried out in all six cities an extensive citizen, stakeholder and policy maker engagement process (Chapter 1). This report summarises the main policy results for the first of the six cities, Bristol (UK). The other ClairCity cities are Amsterdam (NL), Ljubljana (SL), Sosnowiec (PL), CIRA/ Aveiro (PT) and Liguria / Genoa (IT).
ClairCity Project
2019
The ClairCity Horizon2020 project aims to contribute to citizen-inclusive air quality and carbon policy making in middle-sized European cities. It does so by investigating citizens’ current behaviours as well as their preferred future behaviours and policy measures in six European cities1 through an extensive citizen and stakeholder engagement process. The project also models the possible future impacts of citizens’ policy preferences and examines implementation possibilities for these measures in the light of the existing institutional contexts in each city (Figure 0-1). This report summarises the main policy results for Ljubljana.
ClairCity Project
2020
Clean air and healthy lungs. Enhancing the World Bank's Approach to Air Quality Management. Environment and natural resources global practice discussion paper; 03
This report specifically deals with air pollution, which was reported, by the World Health Organization (WHO), as the single largest environmental health risk globally in 2012 (WHO, 2014a). Air pollution from outdoor and household sources jointly account for more than 7 million deaths (3.7 million from ambient air pollution and 4.3 million from household air pollution). The following sections of this chapter present the objectives of, and key aspects of the institutional context for, this report followed by an examination of some of the major drivers of deteriorating ambient air quality in developing countries; air pollution sources and impacts; and the status of air quality management in developing countries. Chapter two presents the results of a desk-based portfolio review of World Bank projects that are relevant to reduction of air pollution. This is followed, in chapter three, by an examination of case studies of World Bank projects whose objectives include addressing ambient air pollution, highlighting good practices and lessons for future work of the Bank in supporting clients. Chapter four presents possible approaches for enhancing future Bank support in helping clients to improve air quality and reduce the associated adverse health outcomes. Chapter five presents overall conclusions and recommendations.
2015
Clean air policies are key for successfully mitigating Arctic warming
A tighter integration of modeling frameworks for climate and air quality is urgently needed to assess the impacts of clean air policies on future Arctic and global climate. We combined a new model emulator and comprehensive emissions scenarios for air pollutants and greenhouse gases to assess climate and human health co-benefits of emissions reductions. Fossil fuel use is projected to rapidly decline in an increasingly sustainable world, resulting in far-reaching air quality benefits. Despite human health benefits, reductions in sulfur emissions in a more sustainable world could enhance Arctic warming by 0.8 °C in 2050 relative to the 1995–2014, thereby offsetting climate benefits of greenhouse gas reductions. Targeted and technically feasible emissions reduction opportunities exist for achieving simultaneous climate and human health co-benefits. It would be particularly beneficial to unlock a newly identified mitigation potential for carbon particulate matter, yielding Arctic climate benefits equivalent to those from carbon dioxide reductions by 2050.
2022
This paper reports estimated maintenance-cleaning costs, cost savings and cleaning interval increases for structural surfaces and windows in Europe obtainable by reducing the air pollution. Methodology and data from the ICP-materials project were used. The average present (2018) cleaning costs for sheltered white painted steel surfaces and modern glass due to air pollution over background, was estimated to be ~2.5 Euro/m2∙year. Hypothetical 50% reduction in the air pollution was found to give savings in these cleaning costs of ~1.5 Euro/m2∙year. Observed reduction in the air pollution, from 2002–2005 until 2011–2014, have probably increased the cleaning interval for white painted steel with ~100% (from 12 to 24 years), representing reductions in the single intervention cleaning costs from 7 to 4%/year (= % of one cleaning investment, per year during the cleaning interval) and for the modern glass with ~65% (from 0.85 to 1.3 years), representing reductions in the cleaning cost from 124 to 95%/year. The cleaning cost reductions, obtainable by 50% reduction in air pollution, would have been ~3 %/year for white painted steel and ~60%/year for the modern glass, representing ~100 and 50% additional cleaning interval increases. These potential cleaning cost savings are significantly higher than previously reported for the weathering of Portland limestone ornament and zinc monuments.
2019