Air Quality Network Research Articles

Integrating Cost-Effective Measurements and CFD Modeling for Accurate Air Quality Assessment

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM10 dispersion in a 1.6 × 2 km2 urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution.

Measurements of the Limit of Detection for Electrochemical Gas Sensors

Abstract Electrochemical amperometric gas cells are becoming the sensor of choice when measuring polluting gases using low-cost air quality networks. A number of technical issues remain to be resolved to deliver fit-for-purpose monitoring systems: humidity corrections are needed but not well understood, interfering gases such as ozone can have variable cross-sensitivity and calibration intervals, and procedures are still being investigated. Another unanswered question is the limit of detection (LOD) for electrochemical gas sensors. Estimates range from hundreds of equivalent parts per billion (ppbv) to single-digit ppbv concentrations. We discuss the LOD for nitrogen dioxide (NO2), an important gas when monitoring air quality. Multiple NO2 sensor systems were tested in an environmental chamber to determine, among other parameters, the LOD for NO2 electrochemical gas sensors. Low-noise electronics and battery powering further reduced electronic noise, allowing the intrinsic LOD of the electrochemical cell to be determined. Noise, quantified as the standard deviation in zero air in a very stable temperature and relative humidity–controlled chamber was <500 pA, which translated into 1.6 ppbv, so the LOD, 3 × standard deviation, was 4.8 ppb. Interestingly, the LOD calculated with 300 ppbv NO2 test gas was the same (±0.1 ppbv). Further tests with a higher resolution analog-to-digital converter resulted in the same LOD, further leading to the conclusion that for the Alphasense NO2-A43F NO2 sensor, the limiting value for LOD is 4.8 ppbv.

Combination of toxicological and epidemiological approaches for estimating the health impact of atmospheric pollutants. A proof of concept for NO2

BackgroundRegular monitoring of the air pollutant nitrogen dioxide (NO2), an indicator for traffic-related emissions, is a priority in urban environments. The health impacts associated with NO2 exposure are the result of a combination of factors, including concentration, duration of exposure, and interactions with other pollutants. WHO has established air quality guidelines based on epidemiological studies. ObjectiveThis study develops a new concept "Health Impact Pathways (HIPs)" using adversity as a probabilistic indicator of health effects. For this purpose, it integrates available toxicological and epidemiological information, using Adverse Outcome Pathways (AOPs), in order to understand chemical-biological interactions and their consequences on health. MethodsLiterature review and meta-analysis of toxicological data supported by expert judgment were performed to establish: a) adversity pathways, b) quantitative criteria for scoring the observed toxicological effects (adversity indicators), c) NO2 exposure - adversity relationship for both long-term (1–36 months) and shortterm (1–7 days). The NO2 daily concentrations from January 2001 to December 2022, were obtained from Madrid city Air Quality network monitoring database. Adversity levels were compared with relative risk levels for all-cause and respiratory mortality estimated using linear equations from WHO 2021 guidelines. ResultsNon-linear relations were obtained for all long- and short-term NO2 related adversity indicators; for long-term effects, the best fitting was obtained with a modified Haber's law model with an exponential coefficient for the exposure time of 0.25. Estimations are presented for a set of case studies for Madrid city, covering temporal and spatial variability. A clear improvement trend along the two decades was observed, as well as high inter- and intra-station variability; the adversity indicators provided integrated information on the temporal and spatial evolution of population level risk. DiscussionThe proposed HIP conceptual approach offers promising advances for integrating experimental and epidemiological data. The next step is linking the concentration-adversity relationship with population health impacts through probability estimations, the preliminary estimations confirm the need for assessing independently different population groups.

Sustained Reductions of Bay Area CO2 Emissions 2018-2022.

Cities represent a significant and growing portion of global carbon dioxide (CO2) emissions. Quantifying urban emissions and trends over time is needed to evaluate the efficacy of policy targeting emission reductions as well as to understand more fundamental questions about the urban biosphere. A number of approaches have been proposed to measure, report, and verify (MRV) changes in urban CO2 emissions. Here we show that a modest capital cost, spatially dense network of sensors, the Berkeley Environmental Air Quality and CO2 Network (BEACO2N), in combination with Bayesian inversions, result in a synthesis of measured CO2 concentrations and meteorology to yield an improved estimate of CO2 emissions and provide a cost-effective and accurate assessment of CO2 emissions trends over time. We describe nearly 5 years of continuous CO2 observations (2018-2022) in a midsized urban region (the San Francisco Bay Area). These observed concentrations constrain a Bayesian inversion that indicates the interannual trend in urban CO2 emissions in the region has been a modest decrease at a rate of 1.8 ± 0.3%/year. We interpret this decrease as primarily due to passenger vehicle electrification, reducing on-road emissions at a rate of 2.6 ± 0.7%/year.

Study of the Interrelations of the Concentrations of Six Tree Pollen Types and Six Atmospheric Pollutants with Rhinitis and Allergic Conjunctivitis in the Community of Madrid

Allergic pathologies of aerobiological origin, specifically those caused by exposure to pollen allergens, have shown a growing trend in recent decades worldwide. This trend is most evident in urban areas experiencing an incessant expansion of their territory. Several studies have shown an interaction between atmospheric pollutants and pollen grains, which implies a potentiation of the allergenicity of the latter. This study aims to analyze the possible influence, in the Community of Madrid (CAM), of the concentrations of six atmospheric pollutants (O3, particles PM10 and PM2.5, NO2, CO, and SO2), and of the pollen concentrations of six types of tree pollen (Cupressaceae, Olea, Platanus, Pinus, Ulmus, and Populus) on the episodes of attention of two pathologies, rhinitis and allergic conjunctivitis. The data collected came from the Air Quality Networks of the CAM and the Madrid City Council, the Palynological Network of the CAM, and the General Subdirectorate of Epidemiology of the Health Department of the CAM. Descriptive multiple linear regression models were used to analyze the interrelationships of the three variables. In most of the calculations performed, the adjusted R2 value is higher than 30%, and, in all cases, the p-values of the models obtained are less than 0.0001. All the models performed in the study period for allergic rhinitis indicate a reasonable correlation, and this is also true for almost all of the models calculated for allergic conjunctivitis. Moreover, it is allergic rhinitis for which the highest values of adjusted R2 were obtained. Pinus is the pollen type most frequently interrelated with conjunctivitis and allergic rhinitis (followed in both cases by Olea and Populus) throughout the study years. In this same period, O3 is the air pollutant most frequently present in the models calculated for allergic conjunctivitis (followed by NO2 and PM10), while particle PM10 is the most frequently included in the calculations made for allergic rhinitis, followed by O3 and SO2.

Assessing the bias of molybdenum catalytic conversion in the measurement of NO2 in rural air quality networks

The measurement method of NO2 with continuous analysers is specified for EU Ambient Air Quality Directive compliance reporting, which provides a consistent methodology and concurrent NO measurements (85/203/EEC-NO2). While the established method of measurement of NO2, following conversion of NO2 to NO using a molybdenum-conversion process, has known interference uncertainties (due to conversion of other oxidised nitrogen (NOy) chemicals, the consistency and traceability of compliance measurement is important. This study compared three continuous NO2 analyser instruments: a Thermo-NOx molybdenum convertor chemiluminescence analyser (Model 42C, ThermoFisher Scientific Inc., MA, USA), a photolytic chemiluminescence analyser (T200UP, Teledyne Technologies Inc., San Diego, USA) and a Cavity Attenuated Phase Shift (CAPS) analyser (T500U, Teledyne Technologies Inc., CA, USA). The instruments were run for over a year at the Auchencorth Moss long-term peatland monitoring site (Southeast Scotland) which is a low NOx atmosphere away from sources. NOy and NHx chemicals were also measured concurrently. This study concludes that there is a strong artefact in molybdenum catalyst chemiluminescent instruments as a result of unselective catalysis of airborne NOy compounds that causes an overestimate of NO2 measured in the atmosphere. The observed artefact in concentration measurements is likely to be observed at the entire UK scale as almost the entirety of the rural air network relies on molybdenum catalyst instruments. We therefore recommend that molybdenum catalyst instruments should be phased out and replaced in air quality monitoring networks with molecule specific (spectroscopy) instrumentation (equivalent in cost, such as those described in this study) that do not suffer from the same measurement artefacts.

Developing an Australian dust early warning system

In Australia, the perception of large continental-scale dust storms has traditionally been restricted to aspects of ‘inconvenience’ or ‘novelty’. However, as the climate changes, there is an increased likelihood of dust storm activity that puts communities at increased risk. The aim of this study was to explore the need and possibility of developing a dust early warning system for Australia. Through a scoping review of internationally used sand and dust early warning systems, we found that an array of systems exist or were theoretically tested in the Northern Hemisphere. The sensor networks, one of which was operational, were of particular interest because Australia already has an operational air quality network that could be expanded to a dust early warning system. The need for a trans-boundary system is critical for Australia and, therefore, requires a combined approach of expanded sensor network with satellite-based systems to increase validation of any future modelling approach. This improved understanding could inform the development of a dust warning system for multi-socio-economic factors that account for cumulative exposure to small, localised and large continental-sized dust events.

Application of CFD Modelling for Pollutant Dispersion at an Urban Traffic Hotspot

Health factors concerning the well-being of the urban population urge us to better comprehend the impact of emissions in urban environments on the micro-scale. There is great necessity to depict and monitor pollutant concentrations with high precision in cities, by constructing an accurate and validated digital air quality network. This work concerns the development and application of a CFD model for the dispersion of particulate matter, CO, and NOx from traffic activity in a highly busy area of the city of Augsburg, Germany. Emissions were calculated based on traffic activity during September of 2018 with COPERT Street software version 2.4. The needed meteorological data for the simulations were taken from a sensor’s network and the resulting concentrations were compared and validated with high-precision air quality station indications. The model’s solver used the steady-state RANS approach to resolve the velocity field and the convection–diffusion equation to simulate the pollutant’s dispersion, each one modelled with different molecular diffusion coefficients. A sensitivity analysis was performed to decide the most efficient computational mesh to be used in the modelling. A velocity profile for the atmospheric boundary layer (ABL) was implemented into the inlet boundary of each simulation. The cases concerned applications on the street level in steady-state conditions for one hour. The results were evaluated based on CFD validation metrics for urban applications. This approach provides a comprehensive state-of-the-art 3D digital pollution network for the area, capable of assessing contamination levels at the street scale, providing information for pollution reduction techniques in urban areas, and combining with existing sensor networks for a more thorough portrait of air quality.

Assessment of environmental risk areas based on airborne pollen patterns as a response to land use and land cover distribution

Allergic respiratory diseases are considered to be among the most important public health concerns, and pollen is the main cause of allergic respiratory diseases worldwide. However, the biological component of air quality is largely underestimated, and there is an important gap in the legislation in this area. The aims of this study were to characterise the occurrence and incidence of pollen exposure in relation to potential pollen sources and to delineate the main areas of aerobiological risk in the Madrid Autonomous Region based on homogeneous patterns of pollen exposure. This study uses the historical aerobiological database of the Madrid Region Palynological Network (central Spain) from ten pollen stations from 1994 to 2022, and the land-use information from the Corine Land Cover. Multiple clustering approaches were followed to group the sampling stations and subsequently all the 1 × 1km pixels for the Madrid Autonomous Region. The clustering dendrogram for land-use distribution was compared to the dendrogram for historical airborne pollen data. The two dendrograms showed a good alignment with a very high correlation (0.95) and very low entanglement (0.15), which indicates a close correspondence between the distribution of the potential pollen sources and the airborne pollen dynamics. Based on this knowledge, the Madrid Autonomous Region was divided into six aerobiological risk areas following a clear anthropogenic gradient in terms of the potential pollen sources that determine pollen exposure in the Madrid Region. Spatial regionalisation is a common practice in environmental risk assessment to improve the application of management plans and optimise the air quality monitoring networks. The risk areas proposed by scientific criteria in the Madrid Autonomous Region can be adjusted to other operational criteria following a framework equivalent to other air quality networks.

Effect of the thermal environment on mortality: analysis of longitudinal data from Cyprus (2009–2018)

Exposure to a suboptimal outdoor thermal environment is associated with increased morbidity and mortality. This study aimed to examine the effect of the thermal environment on all-cause mortality in Cyprus. Daily mortality data were obtained from 2009 to 2018. Hourly meteorological data were retrieved from the ERA5-Land reanalysis database and air pollutant concentrations were obtained from the official air quality network of the Republic of Cyprus. Air temperature (Tair), the physiologically equivalent temperature (PET), and the universal thermal climate index (UTCI) were used to assess the thermal environment. Associations of Tair, PET, and UTCI with daily mortality were examined using negative binomial regression while adjusting for air quality. Mortality in the cold period (December to March) increased by 3.3% for each 1 °C decrease in Tair and by up to 1.8% for each 1 °C decrease in PET and UTCI (p value < 0.001). In the warm period (April to November), a 1 °C increase in Tair or in PET and UTCI was associated with an increase in mortality of up to 1.4% and 1.1% (p value < 0.001), respectively. The effect (increase in mortality) was more pronounced when the analysis focused on winter (3.6% per 1 °C decrease in Tair, 2.4% per 1 °C decrease in PET, and 2% per 1 °C decrease in UTCI) and summer (4.1% per 1 °C increase in Tair, 2.4% per 1 °C increase in PET, and 1.2% per 1 °C increase in UTCI). Males in cold periods and the elderly (older than 64 years) were more vulnerable. Awareness about the effects of the thermal environment on mortality, including those during the cold months of the year, should be enhanced.

Elucidating Best Geospatial Estimation Method Applied to Environmental Sciences

The aim of this study is to assess and identify the most suitable geospatial interpolation algorithm for environmental sciences. The research focuses on evaluating six different interpolation methods using annual average PM10 concentrations as a reference dataset. The dataset includes measurements obtained from a target air quality network (scenery 1) and a sub-dataset derived from a partitive clustering technique (scenery 2). By comparing the performance of each interpolation algorithm using various indicators, the study aims to determine the most reliable method. The findings reveal that the kriging method demonstrates the highest performance within environmental sciences, with a spatial similarity of approximately 70% between the two scenery datasets. The performance indicators for the kriging method, including RMSE (root mean square error), MAE (mean absolute error), and MAPE (mean absolute percentage error), are measured at 3.2 µg/m3, 10.2 µg/m3, and 7.3%, respectively.This study addresses the existing gap in scientific knowledge regarding the comparison of geospatial interpolation techniques. The findings provide valuable insights for environmental managers and decision-makers, enabling them to implement effective control and mitigation strategies based on reliable geospatial information and data. In summary, this research evaluates and identifies the most suitable geospatial interpolation algorithm for environmental sciences, with the kriging method emerging as the most reliable option. The study’s findings contribute to the advancement of knowledge in the field and offer practical implications for environmental management and planning.

Data Gap: Air Quality Networks Miss Air Pollution from Concentrated Animal Feeding Operations.

In the U.S., the agricultural sector is the largest controllable source of several air pollutants, including ammonia (NH3), which is a key precursor to PM2.5 formation. Livestock waste is the dominant contributor to ammonia emissions. In contrast to most controllable air pollutants, satellite records show ammonia mixing ratios are rising. The number of confined animal feeding operations (CAFOs) that generate considerable livestock waste is also increasing. Spatial and temporal trends in USDA-reported animal numbers normalized by county area at medium and large CAFOs provide plausible explanations for patterns in satellite-derived NH3 over the contiguous U.S. (CONUS). The correlation between summertime ammonia derived from the European Space Agency's (ESA) Infrared Atmospheric Sounding Interferometer (IASI) and CAFO animal unit density in 2017 is positive and significant (r = 0.642; p ≈ 0). The temporal changes from 2002 to 2017 in animal unit density and NH3 derived from NASA's Atmospheric Infrared Sounder (AIRS) are spatially similar. Trends and ambient concentrations of PM2.5 mass in agricultural regions are difficult to assess relative to those of urban population centers given the sparseness of rural monitors in regulatory surface networks. Results suggest that in agricultural areas where ammonia concentrations and animal density are highest, air quality improvement lags behind the national average.

Low‐cost air quality monitoring networks for long‐term field campaigns: A review

AbstractThe application of low‐cost air quality monitoring networks has substantially grown over the last few years, following the technological advances in the production of cheap and portable air pollution sensors, thus potentially greatly increasing the limited spatial information on air quality conditions provided by traditional stations. However, the use of low‐cost air quality sensors still presents many limitations, mostly related to the reliability of their measurements. Despite the increasing number of papers focusing on these issues, some of the challenges connected to the use of low‐cost air quality sensors are still poorly investigated and understood, considering in particular those related to long‐term applications of low‐cost air quality networks and their integration within the reference air quality monitoring system. The present review aims at filling this gap, by analysing the characteristics of low‐cost air quality monitoring networks that were run across long‐term field campaigns, including their geographical location, the pollutants monitored, the type and number of stations employed, and the length of the field campaign, with a particular attention on assessing the aims for their deployment and on the evaluation of their integration within official air quality monitoring networks. Moreover, a critical analysis of the most insightful suggestions and recommendations delivered in the literature, as well as of the relevant critical issues, is presented, highlighting still open research areas and outlining future challenges.

Long-Term Analysis of Aerosol Concentrations Using a Low-Cost Sensor: Monitoring African Dust Outbreaks in a Suburban Environment in the Canary Islands.

This study presents the results of the long-term monitoring of PM10 and PM2.5 concentrations using a low-cost particle sensor installed in a suburban environment in the Canary Islands. A laser-scattering Nova Fitness SDS011 sensor was operated continuously for approximately three and a half years, which is longer than most other studies using this type of sensor. The impact of African dust outbreaks on the aerosol concentrations was assessed, showing a significant increase in both PM10 and PM2.5 concentrations during the outbreaks. Additionally, a good correlation was found with a nearby reference instrument of the air quality network of the Canary Islands' government. The correlation between the PM10 and PM2.5 concentrations, the effect of relative humidity, and the stability of the sensor were also investigated. This study highlights the potential of this kind of sensor for long-term air quality monitoring with a view to developing extensive and dense low-cost air quality networks that are complementary to official air quality networks.

Satellite NO2 trends reveal pervasive impacts of wildfire and soil emissions across California landscapes

Nitrogen dioxide (NO2) plays a pivotal role in the production of secondary pollutants, most importantly ozone (O3) and particulate matter. Regulatory controls have greatly reduced NO2 in cities, where most of the surface monitoring occurs, but the change in rural environments is less certain. Here, we present summertime (June–September) spatio-temporal patterns of NO2 concentrations using satellite and ground observations across California from 2009–2020, quantifying the differences in NO2 trends for five distinct land cover classes: urban, forests, croplands, scrublands (shrublands, savannas, and grasslands), and barren (minimally vegetated) lands. Over urban environments, NO2 columns exhibited continued but weakening downward trends (−3.7 ± 0.3%a−1), which agree fairly well with contemporaneous trends estimated from the surface air quality network (−4.5 ± 0.5%a−1). In rural (i.e., non-urban) parts of the state, however, secular trends are insignificant (0.0–0.4 ± 0.4%a−1) or in the case of remote forests are rapidly on the rise (+4.2 ± 1.2%a−1). Sorting the NO2 columns by air temperature and soil moisture reveals relationships that are commensurate with extant parameterizations but do indicate a stronger temperature dependence. We further find that rapidly rising temperatures and, to a lesser extent, decreasing precipitation in response to climate change are acting to increase soil NO x emissions, explaining about one-third of the observed NO2 rise in non-urban regions across California. Finally, we show that these trends, or their absence, can be attributed predominantly to the dramatic rise in wildfire frequency, especially since the turn of the 21st century.

Air Quality Monitoring in Coal-Centric Cities: A Hybrid Approach

Despite the increasing time sensitivity of climate change, many cities worldwide still heavily rely on coal. The extraction, processing, transport, and usage of coal lead to deteriorated air quality, resulting in complex environmental and public health problems for the local communities. Mapping different pollution sources in coal-centric cities is not trivial due to the hyperlocal nature of air pollution and the often low-density network of air quality monitors. This study explores the air quality issues surrounding coal-centric cities using a combination of qualitative and quantitative data from reference-grade air quality monitors, low-cost sensors (LCSs) deployed on citizens’ vehicles, and community engagement activities. It explores how LCSs can be used to characterize air quality at a high spatio-temporal resolution and how this information can be used to decode people’s perceptions of air quality issues and elicit local knowledge. We evaluated our approach in Sparwood (Canada), and Oskemen (Kazakhstan) which are very different cities, but are both heavily dependent on coal. LCSs have been proven an efficient tool to identify pollution hotspots that traditional reference monitors miss, while workshop-based activities making use of data maps and coding tools have successfully elicited information about pollution sources from non-experts, helping collaborative sense-making and informing new LCS deployment strategies. Understanding air quality in coal-centric cities as a complex socio-technical phenomenon can enable the coal industry, city officials, and residents to engage in addressing air quality issues.

Pollution hot spots and the impact of drive-through COVID-19 testing sites on urban air quality

To be successful, commitments to climate change and environmental policy will require critical changes in human behavior and one important example is driving and idling. Idling is defined as running a vehicle’s motor while not in motion. Idling studies have repeatedly demonstrated that this behavior is costly, harmful to human health, and highly polluting. However, with the onset of COVID-19, the use of drive-through services to provide food, pharmaceuticals, and medical testing has increased. To understand this phenomenon further, we worked cooperatively with our government partners to compare the concentrations of PM2.5 at three regulatory sensor locations with nearby drive-through COVID-19 testing sites during average to elevated pollution days. Salt Lake City, UT (USA), where this study was undertaken, has seen a dramatic rise in drive-through services since the onset of the pandemic and community concern is also high due to poor local air quality. More importantly, the Salt Lake Valley is home to one of the largest research grade air quality networks in the world. Fine particulate matter sensors were installed or already in place at or adjacent to COVID-19 testing sites in the area, and we used data from nearby Utah Division of Air Quality monitors to provide comparative PM2.5 concentrations. Due to their placement (e.g., further distance from large roads and other emitting sources), we found that testing sites showed lower PM2.5 concentrations during average air quality days despite increased idling rates. However, when urban pollution rates were elevated due to atmospheric inversions, extensive idling around testing sites led to hyper local PM2.5 concentrations or pollution hot spots. This suggests that idling has serious compounding effects in highly polluted urban areas and policies minimizing vehicle emissions from idling and congestion could conceivably curtail pollutant exposure in a range of settings.

Childhood asthma in the Bronx, NY; the impact of pollutants on length of hospital stay

Objective: The length of hospital stay (LOS) is a proxy of asthma exacerbation severity and healthcare cost. The study aims to estimate the effect of ambient air pollution on pediatric asthma LOS in the Bronx, NY. Methods: A total of 1,920 children admitted to the hospital in Bronx, NY due to asthma during 2017-2019 period were included in the study. Demographic and clinical parameters were obtained from medical records. Daily ozone (O3) and fine particulate matter (PM2.5) measurements were obtained from local air quality networks. Poisson regression adjusting for gender, age, weight status, respiratory infections including influenza, and ambient temperature was applied to determine whether there was an association of air pollution with length of hospital stay. Results: The mean LOS varied by age, sex, weight status, influenza vaccination status, respiratory viral panel (RVP) results, asthma controller use, and asthma classification. After controlling for these factors in Poisson regression, the mean LOS increased up to 10.62% (95%CI: 0.78-21.41; p = 0.03) for an increase of 10 μg/m3 of PM2.5 exposure on admission day, and 3.90% (95%CI = 0.06-7.88; p = 0.05) for an increase of 10 ppbv of O3 concentration during the previous day. Conclusion: Ambient particulate and ozone pollution is associated with lengthier hospital stays for pediatric asthma, potentially indicating more severe asthma exacerbations.

Air-quality networks collect environmental DNA with the potential to measure biodiversity at continental scales

One of the biggest planetary challenges is the accelerating loss of biodiversity threatening ecosystem functioning on a global scale. The WWF Living Planet Report (https://livingplanet.panda.org/) estimates a 69% decline in populations since 1970. The Convention on Biological Diversity and related international treaties ask countries to monitor shifts in community composition and assess rates of species decline to quantify extant biodiversity relative to global targets1. However, quantifying biodiversity is a challenge, and monitoring continual change is impossible at almost any scale due to a lack of standardized data and indicators2,3. A common problem is that the required infrastructure for such global monitoring does not exist. Here, we challenge this notion by analysing environmental DNA (eDNA) captured along with particulate matter by routine ambient air quality monitoring stations in the UK. In our samples, we identified eDNA from >180 vertebrate, arthropod, plant and fungal taxa representative of local biodiversity. We contend that air monitoring networks are in fact gathering eDNA data reflecting local biodiversity on a continental scale, as a result of their routine function. In some regions, air quality samples are stored for decades, presenting the potential for high resolution biodiversity time series. With minimal modification of current protocols, this material provides the best opportunity to date for detailed monitoring of terrestrial biodiversity using an existing, replicated transnational design and it is already in operation.

A Community-Based Sensor Network for Monitoring the Air Quality in Urban Romania

Air quality, especially particulate matter pollution levels in urban areas, is an essential academic and social topic due to its association with health issues and climate change. In Romania, increasing awareness of urban communities and the availability of low-cost sensors has led to the development of an independent monitoring network currently distributed in over 194 cities and towns. The uRADMonitor® network consists of 630 sensors measuring PM10 and PM2.5 concentration levels. The spatial distribution of the sensors complements the national air quality network with sensors in residential areas, intense traffic zones, and industrial areas. The data are available through a user-friendly web-based platform from uRADMonitor®. Based on data collected in 2021, we present an analysis of PM10 pollution levels in Romania’s five most populated urban areas by employing five annual statistical indicators recommended by the European Environmental Agency. For the case of Timișoara, we also compare the data measured by independent sensors with those from the national monitoring network. The results highlight the usefulness of our community-based network as it complements the national one.