The last several decades have seen steady and expansive growth both in the development and the application of low-cost sensors (LCS) in the field of air pollution research. They are now increasingly prevalent in air quality monitoring thanks to their affordability and adaptability across diverse environments. However, in the wider academic and monitoring communities, their deployment has largely focused on the expansion of spatial coverage and generating larger datasets, either to generate data where there previously was none, or to fill spatiotemporal gaps. In this work, we argue that LCS have high potential for use in the targeted assessment of policy interventions, and that this potential remains largely underexplored. Drawing on a number of recent studies, we demonstrate the value of LCS as an effective tool for evaluating the impacts of policy measures on urban air pollution.Utilizing these studies as an empirical basis, this work introduces a five-step rubric for guiding targeted policy assessments using LCS. These steps broadly are: 1. Identification, in which partnerships and policies are identified and established; 2. Planning, in which measurement and intervention timelines are aligned and campaigns designed; 3. Calibration, in which LCS are suitably calibrated using in-situ co-locations for the environments they are to be used in; 4. Analysis, in which LCS data is collected and analysed, with potential impacts of the policy intervention quantified; and 5. Dissemination, in which the results are published and presented in a timely manner to relevant stakeholders. These cyclical steps should be considered fluid and dynamic, as they are intended to align with policymaking timelines, which often diverge from research timelines.In addition, we discuss the strengths and limitations of LCS for use in targeted policy assessment, to clarify key criteria for deployment in this application. These strengths include their capacity for high spatiotemporal resolution, flexible deployment options (especially outdoor), and cost-effectiveness in shorter term campaigns. These attributes enable the detection of hyperlocal pollution patterns and emission events that are often missed by sparsely populated reference monitoring networks. Key limitations to this approach include sensor drift, inter-sensor variability, cross-sensitivities to other pollutants, and the need for rigorous calibration. These factors can constrain the data quality and limit the detection of the impact signal of the policy interventions in question. However, by properly quantifying uncertainties and accounting for e.g., meteorological variability, these limitations can be taken into account and relevant results can still be delivered to stakeholders.As such, this work argues for a shift in the research landscape surrounding LCS, and advocates for a shift away from indiscriminate large-scale sensor deployment and toward targeted assessment of individual policies at the local scale. We encourage its further uptake and remain optimistic that this approach can transform the evidence base for local policy decision-making, to create a step change in the tools and data used for mitigating air pollution and providing clean, healthy air for all.

Air pollution, particularly fine particulate matter (PM2.5), presents a significant environmental health risk in Central Asia, where scientific understanding and monitoring systems remain limited. This study provides a comprehensive analysis of PM2.5 concentrations and their associated health impacts in the capital cities of Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan – Astana, Bishkek, Dushanbe, Ashgabat, and Tashkent – during the period 2010-2019. A fused dataset combining satellite-derived estimates with CAMS reanalysis outputs was developed to produce daily, spatially refined PM2.5 concentrations for the region.All five cities exceeded the WHO annual guideline of 5 µg/m³, with Ashgabat and Tashkent recording the highest annual averages, surpassing 25 µg/m³. Using the AirQ+ model, a Health Impact Assessment (HIA) was performed for chronic obstructive pulmonary disease (COPD) and lung cancer (LC) in adults aged 25 and older. Attributable COPD mortality under the background pollution scenario (2.4 µg/m³) reached up to 34 cases per 100,000 in Bishkek, with attributable proportions ranging from 9% to 21% across cities. LC burdens, although lower in absolute numbers, showed attributable fractions between 9% and 20% in Ashgabat and Tashkent, corresponding to 2 - 3 deaths per 100,000 – values comparable to estimates from studies in similarly polluted urban areas.Scenario analysis revealed that reducing PM2.5 to the WHO guideline of 5 µg/m³ would cut COPD and LC mortality by up to 80% in the most polluted cities. Notably, even moderate reductions, such as reaching the WHO Interim Target-3 of 15 µg/m³, already yielded substantial health benefits. These findings emphasize the urgent need for targeted air quality interventions and stricter regulatory standards across Central Asia.

Black Carbon (BC) is a key component of fine particulate matter that impacts air quality, climate, and public health. Understanding its sources is essential for effective mitigation strategies. This study analyses 5+ years of continuous BC observations in Berlin using Aethalometer AE33 measurements, alongside co-located Particulate Matter (PM₂.₅, PM₁₀), Nitrogen Oxides (NOx), Carbon Monoxide (CO) concentrations, and ultrafine particle (UFP) data. BC source contributions are assessed, with NOx and CO serving as traffic-combustion markers, while biomass burning contributions are examined through seasonalBC variability and its relative contribution is validated with levoglucosan, potassium K+ and/or Elemental Carbon/Organic Carbon (EC/OC) measurements. This study is part of the Net4Cities project, contributing to a broader understanding of urban air pollution dynamics and policy interventions, with a focus on transport sources. Aethalometer wavelength-dependent absorption analysis will be used to to apportion relative contribution of liquid fuel and solid fuel BC, with NOx and CO correlations used to evaluate liquid fuel BC estimates. Multiple assumptions and approaches for source apportionment will be tested to quantify uncertainties and the implications evaluated. Relationships between BC and UFP data are investigated for the final year of data to link BC emissions and particle number concentrations in an urban environment.This work is co-funded by the European Union under Project: 101138405 — Net4Cities, the UK Research and Innovation (UKRI) under the UK government’s Horizon Europe funding guarantee (grant no. 10107404), and the Swiss Secretariat for Education, Research and Innovation (SERI) (grant no. 23.00622).

The rapid spread of the SARS‐CoV‐2 virus lead many European governments to issue stay‐at‐home orders for the sake of controlling its impacts on the health systems. The associated decrease in human activities and therefore emissions provided a unique opportunity for a real world laboratory for atmospheric scientists. The impact on primary emissions, that is, , has been vastly studied but its consequences on secondary pollutants, and secondary organic aerosol, have been reported to a lesser degree and the understanding is more limited. One reason is the chronic imbalance in the attention dedicated to volatile organic compounds. In the present study, we report on the evolution of volatile organic compounds under lockdown conditions in Europe by analyzing the concentrations relayed to the Airbase service of the European Environmental Agency. Subsetting was performed to account for human activity and the influence of meteorology. Traffic or urban stations exhibited the most important reduction in benzene and, more substantially, toluene concentrations. Xylenes, trimethylbenzenes, and ethylbenzene also decreased under lockdown conditions, though less when the synoptic conditions were associated with slow flows. Acyclic alkenes evidenced no change or increased slightly, whereas n‐alkanes increased. The evolution of the relative importance of the sources was investigated by means of diagnostic ratios (toluene to benzene and benzene to toluene to ethylbenzene) and exhibited a shift from traffic toward biomass/biofuel/coal burning, indicating a possible increase in the domestic use of solvents.

Small sensors have the potential to provide valuable complementary measurements to established air quality monitoring stations in urban areas. The flexible deployment options of small sensors also allow for short- or longer-term deployment to e.g., accompany policy implementations. Here we present the results from a number of case studies in Berlin where we used a combination of small sensors and reference instrumentation to assess individual policy’s impacts on local air quality, a metric important to policymakers’ assessments of their success. These measurement campaigns included both the stationary and mobile deployment of small sensors, in collaboration with the city. Data generated by the small sensors were calibrated using co-locations and the open-source 7-step methodology developed in our group (Schmitz, et al. 2021, ACP). Measurements deployed alongside the implementation of several policies captured before-after measurements of nitrogen oxides (NOx) and particulate matter (PM). Data from the urban monitoring network was used to account for changes in meteorology and city-wide changes to assist in isolation of the signal from the policy implementations. Through the implementation of a new bike lane, cyclists’ exposure to NO2 was reduced by 20%; in another case, the closure of a street to vehicle traffic reduced local air pollution to the levels of the urban background. These results were subsequently accounted for by policymakers when determining the success of each measure, considering the implications for human health.

Air pollution continues to be a major global health concern, with nitrogen dioxide (NO2) significantly contributing to negative health impacts. Low-cost sensors (LCS) present promising opportunities for accessible, high-resolution air quality monitoring but are often questioned for their accuracy and reliability. This study assesses the performance of electrochemical LCS for NO2 measurements compared to high-precision reference instruments—cavity attenuated phase shift (CAPS) and chemiluminescence NO2 monitors—across eleven temporal resolutions (ranging from 10 seconds to 6 hours). Data were collected over six months at an urban-traffic air quality monitoring site in Berlin using three EarthSense Zephyr sensor systems equipped with electrochemical sensors. Statistical metrics, including R², relative error (%), and mean bias error (MBE), were used to evaluate sensor performance. The results indicate that LCS demonstrate strong agreement with reference instruments at coarse time resolutions (≥1-hour averages, R² > 0.8), but their accuracy declines considerably at higher resolutions (

In this Perspective, we present the case for low-cost sensors (LCS) to be taken up in a new and specific application: the targeted assessment of individual policies. We present examples in which LCS have been used to this end, discuss their strengths and weaknesses, and provide a rubric for conducting such targeted policy assessments. We encourage the strategic deployment of LCS for the measurement of air quality in this context.

The link between exposure to polluted air and the outcome of diseases (e.g., cardio-vascular diseases, COVID-19) has been established. Nevertheless, research on the quantification of the relationship is still relevant today. Quantifying the link between the ambient atmospheric concentrations of pollutants and the outcome of diseases requires knowledge on the levels of the pollutants through time at various scales. In the present work, an improved and updated version of the APExpose_DE dataset is described. The dataset provides air pollution metrics at the yearly time resolution and at the spatial resolution of the NUTS-3 level, corresponding to the Landkeis/Kreisfreie Stadt in Germany. The dataset evolved from its initial form by expanding the years covered and by refining the gap-filling methodology. The dataset can serve as input to, e.g., observational studies.

In Berlin, Germany, new laws have been passed in the past 5 years seeking to transform the city’s mobility infrastructure to be climate neutral and environmentally friendly. Given Berlin’s size, history, and diverse governance structures, these new mobility measures (e.g. new bike lanes, temporary street closures) are typically implemented piecemeal in heterogeneous districts that makes measuring their individual environmental impacts challenging. Using the transdisciplinary research approach of the Research Institute for Sustainability of the Helmholtz Centre Potsdam (RIFS), the planning and execution of several measurement campaigns, and the subsequent uptake of results into policymaking, was conducted with local stakeholders in the Berlin Senate Department for the Environment, Urban Mobility, Consumer Protection and Climate Action (SenUMVK). To assess individual measures’ impacts on local air quality, a metric important to policymakers’ assessments of their success, before and after measurements of nitrogen oxides (NOx) and particulate matter (PM) were conducted. This talk will focus on the transdisciplinary approach to research and how such an approach can address air pollution and climate change synergies, but also how such an approach facilitates uptake of research results by decision-makers.

Air pollution remains a critical global health challenge, with nitrogen dioxide (NO₂) playing a significant role in adverse health outcomes. Low-cost sensors (LCS) offer promising opportunities for accessible and high-resolution air quality monitoring but face scrutiny over their accuracy and reliability. This study evaluates the performance of electrochemical LCS for NO₂ measurement in comparison to high-precision reference instruments-cavity attenuated phase shift (CAPS) and chemiluminescence NO₂ monitors-at eleven temporal resolutions (between 10-s and 6-h). Using three EarthSense Zephyrs containing electrochemical sensors, data were collected over six months at an urban-traffic air quality monitoring site in Berlin. Sensor performance was assessed based on statistical metrics, including R2, relative error, and mean bias error (MBE). Results revealed that LCS exhibit good agreement with reference instruments at coarse time resolutions (≥1-h averages, R2 > 0.8), but accuracy diminishes significantly at higher resolutions (<1-min, R2 < 0.5). Overall, LCS perform better when trained against CAPS monitors than against chemiluminescence monitors. This performance is largely influenced by chemistry and emissions, with poorer performance during the daytime than at night, a pattern which is exacerbated at high time resolutions. CAPS-calibrated predictive models outperform those calibrated against chemiluminescence monitors in capturing short-term concentration peaks. These findings suggest that while LCS are suitable for coarse-resolution measurements of NO2, their limitations in high temporal resolution dynamic environments pose significant challenges for their use in exposure studies and mobile measurements. Recommendations for improved LCS deployment include careful calibration, strategic experimental design, and focused application on lower time-resolution monitoring.

We propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.

Abstract Background Ambient air pollution is a known risk factor for several chronic health conditions, including pulmonary dysfunction. In recent years, studies have shown a positive association between exposure to air pollutants and the incidence, morbidity, and mortality of a COVID-19 infection, however the time period for which air pollution exposure is most relevant for the COVID-19 outcome is still not defined. The aim of this study was to analyze the difference in association when varying the time period of air pollution exposure considered on COVID-19 infection within the same cohort during the first wave of the pandemic in 2020. Methods We conducted a cross-sectional study analyzing the association between long- (10- and 2-years) and short-term (28 days, 7 days, and 2 days) exposure to NO2 and PM2.5 on SARS-CoV-2 incidence, morbidity, and mortality at the level of county during the first outbreak of the pandemic in spring 2020. Health data were extracted from the German national public health institute (Robert-Koch-Institute) and from the German Interdisciplinary Association for Intensive Care and Emergency Medicine. Air pollution data were taken from the APExpose dataset (version 2.0). We used negative binomial models, including adjustment for risk factors (age, sex, days since first COVID-19 case, population density, socio-economic and health parameters). Results We found that PM2.5 and NO2 exposure 28 days before COVID-19 infection had the highest association with infection, morbidity as well as mortality, as compared to long-term or short-term (2 or 7 days) air pollutant exposure. A 1 μg/m3 increase in PM2.5 was associated with a 31.7% increase in incidence, a 20.6% need for ICU treatment, a 23.1% need for mechanical ventilation, and a 55.3% increase in mortality; an increase of 1 μg/m3 of NO2 was associated with an increase for all outcomes by 25.2 – 29.4%. Conclusions Our findings show a positive association between PM2.5 and NO2 exposure and the clinical course of a SARS-CoV2 infection, with the strongest association to 28 days of exposure to air pollution. This finding provides an indication as to the primary underlying pathophysiology, and can therefore help to improve the resilience of societies by implementing adequate measures to reduce the air pollutant impact on health outcomes. Trial registration Not applicable.

The use of low-cost sensors (LCS) for the evaluation of the ambient pollution by particulate matter (PM) has grown and become significant for the scientific community in the past few years. However promising this novel technology is, the characterization of their limitations is still not satisfactory. Reports in the scientific literature rely on calibration, which implies the physical (or geographical) co-location of the LCS with reference in situ (or remote, e.g. onboard satellite platforms) instrumentation. However, calibration is not always feasible, and even when feasible, the validity of the developed relationship, even in similar settings, is subject to large uncertainties. In the present work, the performance of a popular LCS for PM, the Plantower PMS5003, is investigated. The LCS performs particle counts, which is the physical quantity that is input to the black-box model of the manufacturer to compute the ambient PM mass, which is output to the operator. The particle counts of LCS Plantower PMS5003 units were compared to those of the co-located research-grade Grimm EDM-164 monitor. The results show that humidity possibly has a reduced influence on the performance, but the performance can better be constrained, however spanning more than one order of magnitude in terms of agreement ratio, by functions of the actual particle count itself. In view of these results, further development in the field of LCS for PM monitoring should focus on improvements of the physical design of the devices, in order to enhance the sizing of the particles. The use of the actual Plantower PMS5003 models should be limited to the monitoring of PM mass in the smaller size bins. In terms of particle number distribution, the agreement ratio between a low-cost sensor and a research-grade instrument spans several orders of magnitude. The particle number can be constrained as a function of the reported particle number.

Local policies are part of the toolbox available to decision makers to improve air quality but their effectiveness is underevaluated and underreported. We evaluate the impact of the pedestrianization of a street in the city centre of Berlin on the local air pollution. Nitrogen dioxide (NO _2 ) was measured on the street where the policy was implemented and on two parallel streets using low-cost sensor systems supported by periodic calibrations against reference-grade instruments and constrained by passive samplers. Further measurements of NO _2 were conducted with a reference-grade instrument mounted on a mobile platform. The concentrations were evaluated against the urban background (UB) to isolate the policy-related signal from natural fluctuations, long-term trends and the COVID-19 lockdown. Our analysis shows that the most likely result of the intervention is a reduced NO _2 concentrations to the level of the UB on weekdays for the pedestrian zone. Kerbside NO _2 concentrations exhibited substantial differences to the concentrations measured at lampposts highlighting the difficulty for such measurements to capture personal exposure. The results have implications for policy, showing that an intervention on the local traffic patterns can possibly be effective in improving local air quality.

Urban air pollution remains a challenge in European cities, despite decades of improvement, especially with respect to recent updates to the World Health Organization’s (WHO) air quality guidelines in 2021. At the same time, a new generation of small sensors for air pollution measurement have opened up new avenues for understanding air pollution in cities. In this study, we use Plantower PMS 5003 sensors to measure PM2.5 alongside three local traffic policies implemented in 2020 and 2021. These measures include a new bike-lane and a temporary community space, as well as the creation of a pedestrian zone through the closure of a street to through-traffic. The measurement campaign used the sensors in both mobile and stationary deployments, utilizing their small size and lower cost to increase spatial and temporal resolution measurements. We calibrate the Plantower sensors using Schmitz et al.’s (2021b) methodology and test three different models: multiple linear regression (MLR), gradient-boosting machines (GBM), and support vector machines (SVM). Results show that sensors are useful for measuring PM2.5. We also find no significant effect of any of the local transport policies on local concentrations of PM2.5, despite previous studies of these policies showing reductions in local NO2 concentrations. This indicates that larger-scale policies tackling urban and regional emissions of PM will be needed to improve PM concentrations and meet WHO standards.

The knowledge deficit model does not work, that much is clear. But how can research results be effectively integrated in policy making? While a variety of options exist, this presentation will provide some concrete examples of one approach for how scientific research has successfully informed policy at the local (city) level. Research projects aimed at improving our understanding of urban air pollution and the effect of mobility policies were carried out in Berlin, Germany in collaboration with partners in local authorities. Air quality measurements accompanied the implementation of mobility policies, such as street closures and the implementation of a bike lane, to quantify the influence of the policies on air quality and air pollution exposure for citizens. Steps throughout the research process from concept development to the measurements to the results were designed and accompanied by both scientists and local authorities. Such a collaboration resulted in a more effective uptake of results in policy considerations than similar projects carried out with less engagement throughout. Examples will be provided and used as a framework to discuss some of the practical aspects, including challenges and opportunities, for conducting effective transdisciplinary research.

Emission inventories are a critical basis for air quality and climate modeling, as well as policy decisions. Non-methane volatile organic compounds (NMVOCs) are key precursor compounds in ozone and secondary organic aerosol formation. Accurately representing NMVOCs in emission inventories is crucial for understanding atmospheric chemistry, the impact of policy measures, and climate projections. Improving NMVOC representation in emission inventories is fraught with challenges, ranging from the lack of (long-term) NMVOC measurements, limited efforts in updating emission factors, to the diversity of NMVOC species reactivity. Here we take an initial step to evaluate the representation of urban NMVOC speciation in an emission inventory (EDGARv4.3.2 and EDGARv6.1) at the global level. To compare the urban measurements of NMVOCs to the emission inventory estimates, ratios of individual NMVOCs to acetylene are used. Owing to limitations in measurement data and grouping of NMVOCs in emission inventories, the comparison includes only a limited number of alkanes, alkenes, and aromatics. Results show little to no agreement between the ratios in the observations and those in the global emission inventory for the species compared (r(2) 0.01-0.20). This could be related to incorrect speciation profiles and/or spatial allocation of NMVOCs to urban areas. Regional emission inventories show better agreement among the ratios (r(2) 0.43-0.70). The inclusion of oxygenated species in NMVOC measurements, as well as greater global coverage of measurements could improve representation of NMVOC species in emission inventories, and a mosaic of regional inventories may be a better approach.

Despite improvements in air quality over the last several decades, air pollution will continue to be a leading cause of harmful health effects in European cities as urban populations continue to grow. In recent years, the technology of low-cost sensors (LCS) has been adapted for use in expanding air pollution measurements at higher spatial resolution in cities across the globe. In a novel application, this exploratory study deploys metal oxide (MOS) and electrochemical (EC) low-cost sensors housed in EarthSense Zephyr sensor systems to measure nitrogen dioxide (NO2) and ozone (O3) concentrations in three street canyons in Berlin in winter, spring, and summer from 2017 to 2020. After calibration with reference instrumentation using the seven-step methodology outlined by Schmitz et al. (2021), we compare the measured concentrations with reference and urban background concentrations and investigate relationships with meteorology. We find that, following proper calibration, LCS capture expected patterns of urban pollution in association with diurnal chemistry and meteorology well. Additionally, EC sensors outperform MOS and allow for greater insights into local patterns of pollution. Furthermore, we measure concentrations of NO2 and O3 in street canyons that match expectations from modelling studies, indicating that high spatial resolution deployment of LCS could successfully yield new insights in urban microenvironments and inform model development. While LCS have a wider range of uncertainty than reference instruments, these results suggest that they can be reliably used for several new applications, such as validating urban street canyon models or measuring air pollution alongside changes to urban infrastructure.

Low-cost particulate matter monitors output airborne particle mass concentrations based on optical particle counter sensors. Because the relationship between particle number and mass is complex and varies with time and...

Using low-cost systems to obtain indicative measurements when no calibration is possible.