The Use of Public Data from Low-Cost Sensors for the Geospatial Analysis of Air Pollution from Solid Fuel Heating during the COVID-19 Pandemic Spring Period in Krakow, Poland

Indication Variability of the Particulate Matter Sensors Dependent on Their Location

Particulate matter (PM) suspended in the air significantly impacts human health. Those of anthropogenic origin are particularly hazardous. Poland is one of the countries where the air quality during the heating season is the worst in Europe. Air quality in small towns and villages far from state monitoring stations is often much worse than in larger cities where they are located. Their residents inhale the air containing smoke produced mainly by coal-fired stoves. In the frame of this project, an air quality monitoring network was built. It comprises low-cost PMS7003 PM sensors and ESP8266 microcontrollers with integrated Wi-Fi communication modules. This article presents research results on the influence of the PM sensor location on their indications. It has been shown that the indications from sensors several dozen meters away from each other can differ by up to tenfold, depending on weather conditions and the source of smoke. Therefore, measurements performed by a network of sensors, even of worse quality, are much more representative than those conducted in one spot. The results also indicated the method of detecting a sudden increase in air pollutants. In the case of smokiness, the difference between the mean and median indications of the PM sensor increases even up to 400 µg/m3 over a 5 min time window. Information from this comparison suggests a sudden deterioration in air quality and can allow for quick intervention to protect people's health. This method can be used in protection systems where fast detection of anomalies is necessary.

Air pollution seasons in urban moderate climate areas through big data analytics

High particulate matter (PM) concentrations have a negative impact on the overall quality of life and health. The annual trends of PM can vary greatly depending on factors such as a country's energy mix, development level, and climatic zone. In this study, we aimed to understand the annual cycle of PM concentrations in a moderate climate zone using a dense grid of low-cost sensors located in central Europe (Krakow). Over one million unique records of PM, temperature, humidity, pressure and wind speed observations were analyzed to gain a detailed, high-resolution understanding of yearly fluctuations. The comprehensive big-data workflow was presented with the statistical analysis of the meteorological factors. A big data-driven approach revealed the existence of two main PM seasons (warm and cold) in Europe's moderate climate zone, which do not correspond directly with the traditional four main seasons (Autumn, Winter, Spring, and Summer) with two side periods (early spring and early winter). Our findings also highlighted the importance of high-resolution time and space data for sustainable spatial planning. The observations allowed for distinguishing whether the source of air pollution is related to coal burning for heating in cold period or to agricultural lands burning during the warm period.

Size-Resolved Field Performance of Low-Cost Sensors for Particulate Matter Air Pollution

Particulate matter (PM) air pollution is a major health hazard. The health effects of PM are closely linked to particle size, which governs its deposition in (and penetration through) the respiratory tract. In recent years, low-cost sensors that report particle concentrations for multiple-sized fractions (PM1.0, PM2.5, PM10) have proliferated in everyday use and scientific research. However, knowledge of how well these sensors perform across the full range of reported particle size fractions is limited. Unfortunately, erroneous particle size data can lead to spurious conclusions about exposure, misguided interventions, and ineffectual policy decisions. We assessed the linearity, bias, and precision of three low-cost sensor models, as a function of PM size fraction, in an urban setting. Contrary to manufacturers' claims, sensors are only accurate for the smallest size fraction (PM1). The PM1.0-2.5 and PM2.5-10 size fractions had large bias, noise, and uncertainty. These results demonstrate that low-cost aerosol sensors (1) cannot discriminate particle size accurately and (2) only report linear and precise measures of aerosol concentration in the accumulation mode size range (i.e., between 0.1 and 1 μm). We recommend that crowdsourced air quality monitoring networks stop reporting coarse (PM2.5-10) mode and PM10 mass concentrations from these sensors.

Sensors Network as an Added Value for the Characterization of Spatial and Temporal Air Quality Patterns at the Urban Scale

Within the scope of the Aveiro STEAM City project, an air quality monitoring network was installed in the city of Aveiro (Portugal), to evaluate the potential of sensors to characterize spatial and temporal patterns of air quality in the city. The network consists of nine sensors stations with air quality sensors (PM10, PM2.5, NO₂, O₃ and CO) and two meteorological stations, distributed within selected locations in the city of Aveiro. The analysis of the data was done for a one-year measurement period, from June 2020 to May 2021, using temporal profiles, statistical comparisons with reference stations and Air Quality Indexes (AQI). The analysis of sensors data indicated that air quality variability exists for all pollutants and stations. The majority of the study area is characterized by good air quality, but specific areas-associated with hotspot traffic zones-exhibit medium, poor and bad air quality more frequently. The daily patterns registered are significantly different between the affected and non-affected road traffic sites, mainly for PM and NO₂ pollutants. The weekly profile, significative deltas are found between week and weekend: NO₂ is reduced on the weekends at traffic sites, but PM10 is higher in specific areas during winter weekends, which is explained by residential combustion sources.

The influence of meteorological factors and terrain on air pollution concentration and migration: a geostatistical case study from Krakow, Poland

Despite the very restrictive laws, Krakow is known as the city with the highest level of air pollution in Europe. It has been proven that, due to its location, air pollutants are transported to this city from neighboring municipalities. In this study, a complex geostatistical approach for spatio-temporal analysis of particulate matter (PM) concentrations was applied. For background noise reduction, data were recorded during the COVID-19 lockdown using 100 low-cost sensors and were validated based on indications from reference stations. Standardized Geographically Weighted Regression, local Moran's I spatial autocorrelation analysis, and Getis-Ord Gi* statistic for hot-spot detection with Kernel Density Estimation maps were used. The results indicate the relation between the topography, meteorological variables, and PM concentrations. The main factors are wind speed (even if relatively low) and terrain elevation. The study of the PM2.5/PM10 ratio allowed for a detailed analysis of spatial pollution migration, including source differentiation. This research indicates that Krakow's unfavorable location makes it prone to accumulating pollutants from its neighborhood. The main source of air pollution in the investigated period is solid fuel heating outside the city. The study shows the importance and variability of the analyzed factors' influence on air pollution inflow and outflow from the city.

High-Resolution Urban Air Quality Mapping for Multiple Pollutants Based on Dense Monitoring Data and Machine Learning

Spatially explicit urban air quality information is important for urban fine-management and public life. However, existing air quality measurement methods still have some limitations on spatial coverage and system stability. A micro station is an emerging monitoring system with multiple sensors, which can be deployed to provide dense air quality monitoring data. Here, we proposed a method for urban air quality mapping at high-resolution for multiple pollutants. By using the dense air quality monitoring data from 448 micro stations in Lanzhou city, we developed a decision tree model to infer the distribution of citywide air quality at a 500 m × 500 m × 1 h resolution, with a coefficient of determination (R²) value of 0.740 for PM_2.5, 0.754 for CO and 0.716 for SO₂. Meanwhile, we also show that the deployment density of the monitoring stations can have a significant impact on the air quality inference results. Our method is able to show both short-term and long-term distribution of multiple important pollutants in the city, which demonstrates the potential and feasibility of dense monitoring data combined with advanced data science methods to support urban atmospheric environment fine-management, policy making, and public health studies.

Impact of the ‘Coal-to-Natural Gas’ Policy on Criteria Air Pollutants in Northern China

During the last decades, China had issued a series of stringent control measures, resulting in a large decline in air pollutant concentrations. To quantify the net change in air pollutant concentrations driven by emissions, we developed an approach of determining the closed interval of the deweathered percentage change (DPC) in the concentration of air pollutants on an annual scale, as well as the closed intervals of cumulative DPC in a year compared with that in the base year. Thus, the hourly mean mass concentrations of criteria air pollutants to determine their interannual variations and the closed intervals of their DPCs during the heating seasons from 2013 to 2019 in Qingdao (a coastal megacity) were analyzed. The seasonal mean SO2 concentration decreased from 2013 to 2019. The seasonal mean CO, NO2, and PM2.5 concentrations also generally decreased from 2013 to 2017, but increased unexpectedly in 2018 (from 0.9 mg m−3 (CO), 42 µg m−3 (NO2), and 51 µg m−3 (PM2.5) in 2017 to 1.1 mg m−3, 48 µg m−3, and 64 µg m−3 in 2018, respectively). The closed intervals of DPC in concentrations of CO, NO2, and PM2.5 from the 2017 heating season (2017/2018) to the 2018 heating season (2018/2019) were obtained at (27%, 30%), (15%, 18%), and (30%, 33%), respectively. Such high positive endpoint values of the closed intervals, in contrast to their small interval lengths, indicate increased emissions of these pollutants and/or their precursors in 2018/2019 compared with 2017/2018, by minimizing the meteorological influences. The rebounds of CO, NO2, and PM2.5 in 2018/2019 were likely associated with a doubled increase in natural gas (NG) consumption implemented by the “coal-to-NG” project, as the total energy consumption showed little difference. Our results suggested an important role of the “coal-to-NG” project in driving concentrations of air pollutant increases in China in 2018/2019, which need integrated assessments.

Analysis of Particulate Matter Concentration Changes before, during, and Post COVID-19 Lockdown: A Case Study from Victoria, Mexico

The lockdown measures implemented due to the SARS-CoV-2 pandemic to reduce the epidemic curve, in most cases, have had a positive impact on air quality indices. Our study describes the changes in the concentration levels of PM2.5 and PM10 during the lockdown and post-lockdown in Victoria, Mexico, considering the following periods: before the lockdown (BL) from 16 February to 14 March, during the lockdown (DL) from 15 March to 2 May, and in the partial lockdown (PL) from 3 May to 6 June. When comparing the DL period of 2019 and 2020, we document a reduction in the average concentration of PM2.5 and PM10 of −55.56% and −55.17%, respectively. Moreover, we note a decrease of −53.57% for PM2.5 and −51.61% for PM10 in the PL period. When contrasting the average concentration between the DL periods of 2020 and 2021, an increase of 91.67% for PM2.5 and 100.00% for PM10 was identified. Furthermore, in the PL periods of 2020 and 2021, an increase of 38.46% and 31.33% was observed for PM2.5 and PM10, respectively. On the other hand, when comparing the concentrations of PM2.5 in the three periods of 2020, we found a decrease between BL and DL of −50.00%, between BL and PL a decrease of −45.83%, and an increase of 8.33% between DL and PL. In the case of PM10, a decrease of −48.00% between BL and DL, −40.00% between BL and PL, and an increase of 15.38% between the DL and PL periods were observed. In addition, we performed a non-parametric statistical analysis, where a significant statistical difference was found between the DL-2020 and DL-2019 pairs (x2 = 1.204) and between the DL-2021 and DL-2019 pairs (x2 = 0.372), with a p<0.000 for PM2.5, and the contrast between pairs of PM10 (DL) showed a significant difference between all pairs with p<0.01.

Changes in Air-Pollution-Related Information-Seeking Behaviour during the COVID-19 Pandemic in Poland

Low air quality in Poland is a problem of particularly high urgency. Therefore, Poles must be aware of air quality levels, also during the COVID-19 pandemic. The study aimed to compare air-pollution-related information-seeking behaviour between the pre- and intra-pandemic periods as well as between the actual and theoretical machine-learning-forecasted intra-pandemic models. Google Trends search volumes (GTSVs) in Poland for air-pollution-related keywords were collected between January 2016 and January 2022. To investigate the changes that would have occurred without the outbreak of the pandemic, Seasonal Autoregressive Integrated Moving Average (SARIMA) machine-learning models were trained. Approximately 4,500,000 search queries were analysed. Between pre- and intra-pandemic periods, weighted mean GTSVs changed by −39.0%. When the actual intra-pandemic weighted mean GTSVs were compared to the intra-pandemic forecasts, the actual values were lower by −16.5% (SARIMA’s error = 6.2%). Compared to the pre-pandemic period, in the intra-pandemic period, the number of search queries containing keywords connected with air pollution decreased. Moreover, the COVID-19 pandemic might have facilitated the decrease. Possible causes include an attention shift towards everyday problems connected to the pandemic, worse mental health status and lower outdoor exposure that might have resulted in a lower intensity of non-pandemic-related active information-seeking behaviour.

Air Quality and Behavioral Impacts of Anti-Idling Campaigns in School Drop-Off Zones

Vehicle emissions are a major source of pollution in urban communities and idling may contribute up to 34% or more to local air pollution levels. Reduced idling has been found to be an effective policy tool for improving air quality, especially around schools, where it may also improve outcomes for asthmatic children. We studied two anti-idling campaigns in Salt Lake County, Utah to understand if reduced engine idling leads to behavioral change and subsequent reduction in traffic-related air pollution exposure of the related school. We found a 38% decrease in idling time following an anti-idling campaign and an 11% decrease in the number of vehicles idling at the school drop-off zones. The air quality measurements showed improvement in the middle of the campaign, but seasonal variability as well as atmospheric inversion events had substantial effects on overall ambient pollutant concentrations. This study provides an encouraging starting point to develop more effective anti-idling campaigns to protect the health of children, school staff, and the surrounding community.

Characteristics and Weekend Effect of Air Pollution in Eastern Jilin Province

Using the hourly monitoring data of pollutants from 16 automatic atmospheric monitoring stations in eastern Jilin Province from 2015 to 2020, this paper analyzed the temporal and spatial distribution laws of CO, SO2, NO2, PM10, PM2.5, and O3 in eastern Jilin Province. At the same time, the regional transport pathways of pollutants were analyzed using the hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model; the potential source contribution function (PSCF) analyzed the potential source area of PM2.5. Finally, the “weekend effect” of CO, NO2, PM2.5, and O3 was analyzed. The results showed that the six pollutants showed a downward trend year by year. The concentrations of O3, PM10, and PM2.5 were higher in northwest Jilin, and the concentrations of SO2 and CO were higher in southwest Jilin. Except for CO, the seasonal variation of pollutants was pronounced. Except for O3, most pollutants had the highest concentration in winter. Hourly variation analysis described that SO2 and O3 had only one peak in a day, and the other four pollutants showed “double peak” hourly variation characteristics. The study area was mainly affected by the airflow pathway from northwest and southwest. The weight potential source contribution function (WPSCF) high-value area of PM2.5 was northwest and southwest. O3 showed a “negative weekend effect”, and NO2 and CO showed a “positive weekend effect”.

Estimation and Analysis of PM2.5 Concentrations with NPP-VIIRS Nighttime Light Images: A Case Study in the Chang-Zhu-Tan Urban Agglomeration of China

Rapid economic and social development has caused serious atmospheric environmental problems. The temporal and spatial distribution characteristics of PM_2.5 concentrations have become an important research topic for sustainable social development monitoring. Based on NPP-VIIRS nighttime light images, meteorological data, and SRTM DEM data, this article builds a PM_2.5 concentration estimation model for the Chang-Zhu-Tan urban agglomeration. First, the partial least squares method is used to calculate the nighttime light radiance, meteorological elements (temperature, relative humidity, and wind speed), and topographic elements (elevation, slope, and topographic undulation) for correlation analysis. Second, we construct seasonal and annual PM_2.5 concentration estimation models, including multiple linear regression, support random forest, vector regression, Gaussian process regression, etc., with different factor sets. Finally, the accuracy of the PM_2.5 concentration estimation model that results in the Chang-Zhu-Tan urban agglomeration is analyzed, and the spatial distribution of the PM_2.5 concentration is inverted. The results show that the PM_2.5 concentration correlation of meteorological elements is the strongest, and the topographic elements are the weakest. In terms of seasonal estimation, the spring estimation results of multiple linear regression and machine learning estimation models are the worst, the winter estimation results of multiple linear regression estimation models are the best, and the annual estimation results of machine learning estimation models are the best. At the same time, the study found that there is a significant difference in the temporal and spatial distribution of PM_2.5 concentrations. The methods in this article overcome the high cost and spatial resolution limitations of traditional large-scale PM_2.5 concentration monitoring, to a certain extent, and can provide a reference for the study of PM_2.5 concentration estimation and prediction based on satellite remote sensing technology.

EU Net-Zero Policy Achievement Assessment in Selected Members through Automated Forecasting Algorithms

The European Union (EU) has positioned itself as a frontrunner in the worldwide battle against climate change and has set increasingly ambitious pollution mitigation targets for its members. The burden is heavier for the more vulnerable economies in Central and Eastern Europe (CEE), who must juggle meeting strict greenhouse gas emission (GHG) reduction goals, significant fossil-fuel reliance, and pressure to respond to current pandemic concerns that require an increasing share of limited public resources, while facing severe repercussions for non-compliance. Thus, the main goals of this research are: (i) to generate reliable aggregate GHG projections for CEE countries; (ii) to assess whether these economies are on track to meet their binding pollution reduction targets; (iii) to pin-point countries where more in-depth analysis using spatial inventories of GHGs at a finer resolution is further needed to uncover specific areas that should be targeted by additional measures; and (iv) to perform geo-spatial analysis for the most at-risk country, Poland. Seven statistical and machine-learning models are fitted through automated forecasting algorithms to predict the aggregate GHGs in nine CEE countries for the 2019–2050 horizon. Estimations show that CEE countries (except Romania and Bulgaria) will not meet the set pollution reduction targets for 2030 and will unanimously miss the 2050 carbon neutrality target without resorting to carbon credits or offsets. Austria and Slovenia are the least likely to meet the 2030 emissions reduction targets, whereas Poland (in absolute terms) and Slovenia (in relative terms) are the farthest from meeting the EU’s 2050 net-zero policy targets. The findings thus stress the need for additional measures that go beyond the status quo, particularly in Poland, Austria, and Slovenia. Geospatial analysis for Poland uncovers that Krakow is the city where pollution is the most concentrated with several air pollutants surpassing EU standards. Short-term projections of PM2.5 levels indicate that the air quality in Krakow will remain below EU and WHO standards, highlighting the urgency of policy interventions. Further geospatial data analysis can provide valuable insights into other geo-locations that require the most additional efforts, thereby, assisting in the achievement of EU climate goals with targeted measures and minimum socio-economic costs. The study concludes that statistical and geo-spatial data, and consequently research based on these data, complement and enhance each other. An integrated framework would consequently support sustainable development through bettering policy and decision-making processes.

Mortality Related to Air Temperature in European Cities, Based on Threshold Regression Models

There is a wealth of scientific literature that scrutinizes the relationship between mortality and temperature. The aim of this paper is to identify the nexus between temperature and three different causes of mortality (i.e., cardiological, respiratory, and cardiorespiratory) for three countries (Scotland, Spain, and Greece) and eleven cities (i.e., Glasgow, Edinburgh, Aberdeen, Dundee, Madrid, Barcelona, Valencia, Seville, Zaragoza, Attica, and Thessaloniki), emphasizing the differences among these cities and comparing them to gain a deeper understanding of the relationship. To quantify the association between temperature and mortality, temperature thresholds are defined for each city using a robust statistical analysis, namely threshold regression analysis. In a more detailed perspective, the threshold used is called Minimum Mortality Temperature (MMT), the temperature above or below which mortality is at minimum risk. Afterward, these thresholds are compared based on the geographical coordinates of each city. Our findings show that concerning all-causes of mortality under examination, the cities with higher latitude have lower temperature thresholds compared to the cities with lower latitude. The inclusion of the relationship between mortality and temperature in the array of upcoming climate change implications is critical since future climatic scenarios show an overall increase in the ambient temperature.

Can Changes in Urban Form Affect PM2.5 Concentration? A Comparative Analysis from 286 Prefecture-Level Cities in China

It is crucial to the sustainable development of cities that we understand how urban form affects the concentration of fine particulate matter (PM2.5) from a spatial–temporal perspective. This study explored the influence of urban form on PM2.5 concentration in 286 prefecture-level Chinese cities and compared them from national and regional perspectives. The analysis, which explored the influence of urban form on PM2.5 concentration, was based on two types of urban form indicators (socioeconomic urban index and urban landscape index). The results revealed that cities with high PM2.5 concentrations tended to be clustered. From the national perspective, urban built-up area (UA) and road density (RD) have a significant correlation with PM2.5 concentration for all cities. There was a significant negative correlation between the number of patches (NP) and the average concentration of PM2.5 in small and medium-sized cities. Moreover, urban fragmentation had a stronger impact on PM2.5 concentrations in small cities. From a sub-regional perspective, there was no significant correlation between urban form and PM2.5 concentration in the eastern and central regions. On the other hand, the influence of population density on PM2.5 concentration in northeastern China and northwestern China showed a significant positive correlation. In large- and medium-sized cities, the number of patches (NP), the largest patch index (LPI), and the contagion index (CONTAG) were also positively correlated with PM2.5 concentration, while the LPI in small cities was significantly negatively correlated with PM2.5 concentration. This shows that, for more developed areas, planning agencies should encourage moderately decentralized and polycentric urban development. For underdeveloped cities and shrinking cities, the development of a single center should be encouraged.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing–Tianjin–Hebei Region, China

Understanding the spatiotemporal characteristics of PM_2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM_2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM_2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m⁻³) in 2019. PM_2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM_2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM_2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM_2.5 pollution.

Evaluating the Utility of High-Resolution Spatiotemporal Air Pollution Data in Estimating Local PM2.5 Exposures in California from 2015–2018

Air quality management is increasingly focused not only on across-the-board reductions in ambient pollution concentrations but also on identifying and remediating elevated exposures that often occur in traditionally disadvantaged communities. Remote sensing of ambient air pollution using data derived from satellites has the potential to better inform management decisions that address environmental disparities by providing increased spatial coverage, at high-spatial resolutions, compared to air pollution exposure estimates based on ground-based monitors alone. Daily PM2.5 estimates for 2015–2018 were estimated at a 1 km2 resolution, derived from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instrument and the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in order to assess the utility of highly refined spatiotemporal air pollution data in 92 California cities and in the 13 communities included in the California Community Air Protection Program. The identification of pollution hot-spots within a city is typically not possible relying solely on the regulatory monitoring networks; however, day-to-day temporal variability was shown to be generally well represented by nearby ground-based monitoring data even in communities with strong spatial gradients in pollutant concentrations. An assessment of within-ZIP Code variability in pollution estimates indicates that high-resolution pollution estimates (i.e., 1 km2) are not always needed to identify spatial differences in exposure but become increasingly important for larger geographic areas (approximately 50 km2). Taken together, these findings can help inform strategies for use of remote sensing data for air quality management including the screening of locations with air pollution exposures that are not well represented by existing ground-based air pollution monitors.