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This document is an editorial from the journal "Air Quality & Climate Change." It discusses various research papers on air pollution measurements and their importance in understanding and addressing the impacts of air pollution on human health and the environment. The editorial also mentions the need for more measurements, particularly in relation to changes in Australia's road vehicle fleet and the potential increase in pollution costs. Additionally, the editorial highlights the use of selected ion flow tube mass spectrometry and drone sampling for monitoring hydrocarbon emissions in South Korea. The author expresses gratitude to the authors and reviewers of the papers and hopes that readers find the issue interesting and stimulating. [Extracted from the article]

Urban air quality has significant and far-reaching impacts on both human health and the broader environment. Pollutants like particulate matter (PM2.5 and PM10), for instance, are associated with a range of health issues including respiratory conditions, asthma, heart diseases, and even contribute to low birth weight in newborns. These health implications extend to larger environmental concerns such as air pollution, greenhouse gas emissions, and global climate change. Recognizing the urgent need for effective and dynamic air quality monitoring solutions, this paper explores the use of smart environmental drones as a promising approach. Our drone is equipped with a state-of-the-art, low-cost particulate matter sensor that can accurately measure PM2.5 and PM10 concentrations. Operating at a flight speed of 10 m/s and capable of covering a range of 5 km, the drone executes a pre-programmed flight plan to autonomously map pollution levels across urban areas. With a 95% accuracy rate in sensor readings, our model significantly minimizes potential errors commonly associated with traditional air quality monitoring methods. Furthermore, it simplifies maintenance procedures, reducing both time and financial costs. By employing drone technology in this innovative manner, our model offers a cost-effective, reliable, and dynamic solution for monitoring urban air quality. It provides real-time, actionable pollution indices that can inform public health decisions, regulatory policies, and community awareness, thereby contributing to the broader goal of improving air quality and public health. [ABSTRACT FROM AUTHOR]

Sustainable development and the creation of smart, green cities requires cooperation in many scientific fields, including those related to ecology, mobility, or sustainable management, among others. Environmental protection is a particularly important element here. Atmospheric pollution, due to air movements, spreads over very large areas; therefore, air quality monitoring is crucial to ensure protection from harmful substances. One of the most severe sources of air pollution, accounting for as much as approximately 25% of total annual emissions within the EU, is road transport. Therefore, the European Union has set an ambitious target to reduce total emissions to 55% for cars and 50% for vans by 2030. In recent years, unmanned aerial vehicles (UAVs) have become increasingly popular in many scientific fields, including environmental protection and photogrammetry. The use of UAVs to identify harmful pollutants allows them to gain an advantage over conventional detection methods, due to the possibility of remote (therefore safe for humans), faster, and area-based measurements. Given the ever-expanding scale of the use of this technology, this paper presents the possibilities of using UAVs to identify and visualize (map) pollution. The examples presented in the foreign literature, as well as our own research, in imaging the altitude distribution of air pollutants; gaseous pollutants: C6H6, HCHO, SO2; and particulate matter: PM1, PM2.5, PM10 demonstrate the validity of such measures. This research was carried out in the area of one of Poland's key A4 highways. The maps obtained allow for an area-wise and altitude-wise presentation of one of the significant air pollutants in the EU. In addition, they can be a valuable source of information for the implementation of future projects and the improvement of road infrastructure, thus contributing to the reduction of air pollution and the creation of so-called "green cities". [ABSTRACT FROM AUTHOR]

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We use air pollution exposure measurements and household survey data from four studies conducted across three countries in sub‐Saharan Africa (SSA) to analyze the association between carbon monoxide (CO) exposure from cooking with biomass and indicators of cognitive impairment. While there is strong evidence on the relationship between ambient air pollution exposure and cognitive impairment from studies in high‐income countries, relatively little research has focused on household air pollution (HAP) in low‐income country settings where risks of HAP exposure are high. This study is the first to our knowledge to focus on the association between HAP exposure (specifically CO exposure) and cognitive impairment across diverse settings in SSA. We use 24‐hour measurements of primary cooks' exposure to CO across four study sites: urban Zambia (n = 493); urban Malawi (n = 130); rural Malawi (n = 102); and urban Rwanda (n = 2,576). We model the estimated percent carboxyhemoglobin (%COHb) of cooks and map values to a toxicological profile for risk of cognitive impairment. We find that across all study settings, cooks' average %COHb levels are below levels of daily concern, but that cooks who use charcoal for preparing greater than 40% of meals are more likely to spend additional time at higher levels of risk. For the urban Zambia sample, we compare %COHb and frequency of charcoal use to a series of cognitive test scores and find no consistent relationships between %COHb and cognitive test scores. High levels of daily CO exposure from cooks across SSA highlight the potential for longer‐term negative cognitive (and other) health outcomes motivating additional research and efforts to characterize and mitigate risk. [ABSTRACT FROM AUTHOR]

In numerous developing nations, the pervasive practice of crop residue incineration is a principal contributor to atmospheric contamination in agricultural operations. This study examines the repercussions of such biomass combustion on air quality during the autumnal harvest season, utilizing data acquired from satellite-based remote sensing of fire events and air pollution measurements. Employing wind direction information alongside difference-in-difference and fixed-effects methodologies, this investigation rectifies estimation inaccuracies stemming from the non-random distribution of combustion occurrences. The empirical findings reveal that agricultural residue burning precipitates an elevation in average PM2.5 and PM10 concentrations by approximately 27 and 22 μg/m3 during the autumnal incineration period, respectively. Furthermore, air pollution attributed to residue burning in prominent grain-producing regions exceeds the national average by approximately 40%. By integrating economic paradigms into agri-environmental inquiries, this study offers novel insights and substantiation of the environmental expenditures engendered by crop residue burning, juxtaposed with extant meteorological and ecological research findings. [ABSTRACT FROM AUTHOR]

Vertical profiles of atmospheric pollutants, acquired by unmanned aerial vehicles (UAVs, known as drones), represent a new type of observation that can help to fill the existing observation gap in the planetary boundary layer. In this article, the first study of assimilating air pollutant observations from drones is presented to evaluate the impact on local air quality analysis. The study uses the high-resolution air quality model EURAD-IM (EURopean Air pollution Dispersion – Inverse Model), including the four-dimensional variational data assimilation system (4D-var), to perform the assimilation of ozone (O3) and nitrogen oxide (NO) vertical profiles. 4D-var takes advantage of the inverse technique and allows for simultaneous adjustments of initial values and emissions rates. The drone data was collected during the MesSBAR (Automatisierte luftgestützte Mes sung der S chadstoff B elastung in der erdnahen A tmosphäre in urbanen R äumen / Automated airborne measurement of air pollution levels in the near earth atmosphere in urban areas) field campaign, which was conducted on 22–23 September 2021 in Wesseling, Germany. The two-day analyses reveal that the 4D-var assimilation of high-resolution drone measurements has a beneficial impact on the representation of regional air quality in the model. On both days, a significant improvement in the vertical distribution of O3 and NO is noticed in the analysis compared to the reference simulation without data assimilation. Moreover, the validation against independent observations shows an overall improvement in the bias, root-mean-square error, and correlation for O3, NO, and NO2 (nitrogen dioxide) ground concentrations at the measurement site as well as in the surrounding region. Furthermore, the assimilation allows for the deduction of emission correction factors in the grid cells surrounding the measurement site, which significantly contribute to the observed improvement in the analysis. [ABSTRACT FROM AUTHOR]

This study evaluates tropospheric columns of methane, carbon monoxide, and ozone in the Arctic simulated by 11 models. The Arctic is warming at nearly 4 times the global average rate, and with changing emissions in and near the region, it is important to understand Arctic atmospheric composition and how it is changing. Both measurements and modelling of air pollution in the Arctic are difficult, making model validation with local measurements valuable. Evaluations are performed using data from five high-latitude ground-based Fourier transform infrared (FTIR) spectrometers in the Network for the Detection of Atmospheric Composition Change (NDACC). The models were selected as part of the 2021 Arctic Monitoring and Assessment Programme (AMAP) report on short-lived climate forcers. This work augments the model–measurement comparisons presented in that report by including a new data source: column-integrated FTIR measurements, whose spatial and temporal footprint is more representative of the free troposphere than in situ and satellite measurements. Mixing ratios of trace gases are modelled at 3-hourly intervals by CESM, CMAM, DEHM, EMEP MSC-W, GEM-MACH, GEOS-Chem, MATCH, MATCH-SALSA, MRI-ESM2, UKESM1, and WRF-Chem for the years 2008, 2009, 2014, and 2015. The comparisons focus on the troposphere (0–7 km partial columns) at Eureka, Canada; Thule, Greenland; Ny Ålesund, Norway; Kiruna, Sweden; and Harestua, Norway. Overall, the models are biased low in the tropospheric column, on average by - 9.7 % for CH 4 , - 21 % for CO, and - 18 % for O 3. Results for CH 4 are relatively consistent across the 4 years, whereas CO has a maximum negative bias in the spring and minimum in the summer and O 3 has a maximum difference centered around the summer. The average differences for the models are within the FTIR uncertainties for approximately 15 % of the model–location comparisons. [ABSTRACT FROM AUTHOR]