Do new bike lanes impact air pollution exposure for cyclists?—a case study from Berlin

Cities in the 21st century are dynamically changing in response to environmental and societal pressures, not least among which are climate change and air pollution. In some of these metropoles, such as Berlin, a transformation of mobility systems has already begun. Along a mid-sized street in Berlin, a measurement campaign was conducted in 2020 to accompany the construction of a bike lane and the implementation of a community space along one of the side-streets. Using the new technology of low-cost sensors, higher resolution measurements of local air quality were enabled. Stationary and mobile measurements were taken using EarthSense Zephyr sensor systems before and after the construction of the bike lane and during the timeframe when the community space was in place. It was found that the implementation of the bike lane led to a reduction in NO _2 exposure for cyclists. During periods when the community space was in place, a reduction in NO _2 concentrations was also measured. This study highlights not only the utility of low-cost sensors for the measurement of urban air quality, but also their value in a science-policy context. Measuring local air quality changes in response to traffic interventions will enhance understanding of the associated health benefits, especially in connection with measures promoting more sustainable modes of active travel. More research of this nature is needed to gain a clear understanding of the impacts of traffic interventions on local air quality for better protection of human health

Determinants of Public Acceptance for Traffic-Reducing Policies to Improve Urban Air Quality

Air pollution remains a problem in German cities. In particular, the nitrogen dioxide (NO2) annual limit-value set by the European Union of 40 µg/m3 was not met at ~40% of roadside monitoring stations across German cities in 2018. In response to this issue, many cities are experimenting with various traffic-reducing measures targeting diesel passenger vehicles so as to reduce emissions of NO2 and improve air quality. Identifying the determinants of public acceptance for these measures using a systematic approach can help inform policy-makers in other German cities. Survey data generated from a questionnaire in Potsdam, Germany, were used in predictive models to quantify support for investments in traffic-reducing measures generally and to quantify support for a specific traffic-reducing measure implemented in Potsdam in 2017. This exploratory analysis found that general support for investments in such measures was most strongly predicted by environmental and air pollution perception variables, whereas specific support for the actual traffic measure was most strongly predicted by mobility habits and preferences. With such measures becoming more common in German cities and across Europe, these results exemplify the complexity of factors influencing public acceptance of traffic-reducing policies, highlight the contrasting roles environmental beliefs and mobility habits play in determining support for such measures, and emphasize the connections between mobility, air pollution, and human health

Climate change and air pollution: the connection between traffic intervention policies and public acceptance in a local context

Urban mobility is the main source of air pollution in Europe and accounts for 25% of greenhouse gas emissions. In order to address this, a range of interventions and policies are being implemented across major European cities and studies in sustainable urban transport have proliferated. One such mitigation strategy involves redesigning urban form through ‘hard’ traffic policies, with a view of decreasing emission levels and therefore mitigating the effects of air pollution and climate change. However, efforts to assess public response to such interventions and the effectiveness of policy instruments in promoting sustainable travel in cities remain sparse. The city of Potsdam, Germany implemented a trial traffic measure aimed at reducing motorized traffic and promoting the use of bicycles and public transport systems. This study analysed data from 3553 survey participants who responded to a survey conducted prior to the implementation of the traffic measure. We aimed to identify mobility behaviours and underlying attitudes within the context of a ‘hard’ policy instrument, in order to obtain insight into the opportunities to more effectively define policy priorities that improve air quality and upscale climate mitigation. An exploratory cluster analysis identified four groups, characterised by mobility habits, their attitudes towards the measure, and general level of environmental concern. By identifying and understanding the differing attitudes and perceptions across population groups we are able to highlight group-specific barriers and opportunities, as well as potential transition pathways to encourage more sustainable transportation use. This study exemplifies how context can help to further shape mobility group typologies, identify policy-related priorities useful for decision-makers and assess the feasibility of policy instruments to facilitate a transformation towards more sustainable cities

Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors

The last 2 decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, the air quality monitoring community, and for commercial and private use, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensor systems using reference instrumentation with user-friendly guidelines, open-access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both multiple linear regression (MLR) and the random forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOSs). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensor systems, we suggest a number of best practices for future studies using low-cost sensor systems to ensure greater comparability of research

Unravelling a black box: an open-source methodology for the field calibration of small air quality sensors

The last 2 decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, the air quality monitoring community, and for commercial and private use, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensor systems using reference instrumentation with user-friendly guidelines, open-access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both multiple linear regression (MLR) and the random forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOSs). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensor systems, we suggest a number of best practices for future studies using low-cost sensor systems to ensure greater comparability of research

Harden up: metal acquisition in the weaponized ovipositors of aculeate hymenoptera

The use of metal ions to harden the tips and edges of ovipositors is known to occur in many hymenopteran species. However, species using the ovipositor for delivery of venom, which occurs in the aculeate hymenoptera (stinging wasps, ants, and bees) remains uninvestigated. In this study, scanning electron microscopy coupled with energy-dispersive X-ray analysis was used to investigate the morphology and metal compositional differences among aculeate aculei. We show that aculeate aculei have a wide diversity of morphological adaptations relating to their lifestyle. We also demonstrate that metals are present in the aculei of all families of aculeate studied. The presence of metals is non-uniform and concentrated in the distal region of the stinger, especially along the longitudinal edges. This study is the first comparative investigation to document metal accumulation in aculeate aculei