Air Pollution Measurements Research Articles

Integrated ground and vertical measurement techniques to characterize overhead atmosphere: Case studies of local versus transboundary pollution

Much attention has been found to the long-range transport (LRT) of air pollutants and their adverse effects on downwind air qualities resulting from the Chinese haze, which frequently occurs in association with winter monsoon. This study integrates ground-based measurements, unmanned aerial vehicles (UAVs), and model simulations to characterize the meteorological, chemical, and particulate matter (PM) properties comprehensively for the events that were LRT or local pollution (LP) dominated in northern Taiwan during the wintertime of 2017. During the two types of episodes, various approaches were made to investigate the vertical mixing conditions and PM properties with UAV flights. A confined and PM accumulated feature near ground level with a temperature inversion was found during the LP event. In contrast, a vertically homogeneous atmospheric structure with strong winds was suggested during the LRT event. Independent measurements of criteria air pollutants, meteorological variables, volatile organic compounds (VOCs), and micropulse lidar (MPL) made at the ground level were closely supported by the vertical measurements. When synchronizing all these observational and numerical tools in a three-dimensional manner, the characterization of air masses and possible origins of pollution, such as LP vs. LRT, has now become more versatile and capable of gaining a complete picture of atmospheric conditions that define air quality.

Calibrating low-cost sensors to measure vertical and horizontal gradients of NO2 and O3 pollution in three street canyons in Berlin

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.

Associations Between Ambient Air Pollution and Cognitive Abilities from Midlife to Early Old Age: Modification by APOE Genotype.

Fine particulate matter (PM2.5) and nitrogen dioxide (NO2) measures of ambient air pollution are associated with accelerated age-related cognitive impairment, and Alzheimer's disease and related dementias (ADRD). We examined associations between air pollution, four cognitive factors, and the moderating role of apolipoprotein E (APOE) genotype in the understudied period of midlife. Participants were ∼1,100 men in the Vietnam Era Twin Study of Aging. Baseline cognitive assessments were from 2003 to 2007. Measures included past (1993-1999) and recent (3 years prior to baseline assessment) PM2.5 and NO2 exposure, in-person assessment of episodic memory, executive function, verbal fluency, and processing speed, and APOE genotype. Average baseline age was 56 years with a 12-year follow-up. Analyses adjusted for health and lifestyle covariates. Performance in all cognitive domains declined from age 56 to 68. Higher PM2.5 exposures were associated with worse general verbal fluency. We found significant exposure-by-APOE genotype interactions for specific cognitive domains: PM2.5 with executive function and NO2 with episodic memory. Higher PM2.5 exposure was related to worse executive function in APOE ɛ4 carriers, but not in non-carriers. There were no associations with processing speed. These results indicate negative effects of ambient air pollution exposure on fluency alongside intriguing differential modifications of cognitive performance by APOE genotype. APOE ɛ4 carriers appeared more sensitive to environmental differences. The process by which air pollution and its interaction with genetic risk for ADRD affects risk for later life cognitive decline or progression to dementia may begin in midlife.

Air pollution and entrepreneurship: evidence from China

ABSTRACT Air pollution has become a major danger to human health and a hindrance to social and economic growth. This study endeavours to examine the impact of air pollution on entrepreneurial activities in China. Using registration data from 2000 to 2018, we find that severe air pollution can decrease the number of newly registered firms by about 36%, suggesting a negative effect on entrepreneurship. To strengthen the causal inference, we adopt the two-stage least square method to address the potential endogenous problems. The result is robust to different model settings and alternative measurements of air pollution. The study further delves into the mechanisms through which air pollution affects entrepreneurial activities, including brain drain, social capital, startup costs, and financial constraints. This study recommends effective environmental regulation policies to improve air quality as well as increase the attraction of mobile factors to alleviate the adverse impacts of air pollution on entrepreneurial activities.

Recommendations on the measurement techniques of atmospheric pollutants from in situ and satellite observations: a review

Measurement of atmospheric air pollutants (aerosols, NO2, O3, CO, HCHO, and SO2) is essential for characterizing the environmental and biogeochemical process to monitor the air quality. The concerned measurement methods and instrumentations are complex due to the weak spectral characteristics and very low concentration in volume mixtures of them in the atmosphere. This narrative review focuses on the fundamental and advanced concepts of almost all the in situ and satellite-based methods and instrumentations for measuring the gaseous and other air pollutants in the earth-atmosphere interaction. This paper discusses the literature of past and present developments in the measurement methods and instrumentations by highlighting their positive and negative feedbacks. The developing history (1970–2020) of space-borne instrumentations is indicated along with their techniques and capability to compute the concentration of the atmospheric pollutants for monitoring the air quality (air pollution) and climate in large (regional to global) scale. Several applications of the satellite instruments are described in terms of some important pollutant gases, most of which are known as ambient air pollutant. This study makes some recommendations to the readers so that they can utilize the measurement methods and instrumentations to estimate the important air pollutants such as NO2, SO3, O3, CO, and aerosols as well as for setting up future guidelines and air pollution control policies regarding the epidemiology and public health issues.

Investigating the Impact of Construction in Urban Areas on Air Pollution in the Troposphere Using Sentinel Satellite Images: A Case Study in District 22 West of Tehran, Iran

In recent years removing environmental pollution has become one of the main concerns of human life. Air quality in cities depends on weather conditions and the amount of pollutants produced. Today, air pollution is one of the most complex problems of human societies, which has left many negative effects on the health of living beings, especially humans. In this research, the average monthly and annual concentrations of air contaminators in the air pollution measurement stations of the General Directorate of Environmental Protection of Tehran province in the years 2013 to 2019 were examined. By using google earth engine site [1]. Processes that GEE (Google Earth Engine) contains a consolidated resource of open-access RS (Remote Sensing) datasets, along with a variety of algorithms to extract information for Earth’s surface monitoring [2], and using maximum likelihood classification method it recognized that examining the spatial changes of pollutants in the areas where there was more construction, the amount of contaminators was also higher. In terms of time, however, no special changes have been observed for all gases. For AI (Aerosol Index) pollutant, no very specific changes were observed within the region. We have seen a growing trend for carbon monoxide pollutant. For the nitrogen oxide pollutant, we have not seen any growth during this period of time. For sulfur dioxide, we have seen a growing trend. Also, in terms of seasonal changes, air pollutants are more concentrated in winter.

Mobile measurement of vehicle emission factors in a roadway tunnel: A concentration gradient approach

Tunnels are the preferred experimental environments for estimating vehicle emission factors (EFs) under real-world driving conditions. In this study, online measurements of traffic-related air pollutants (including CO2, NOX, SO2, O3, particulate matter [PM], and volatile organic compounds [VOCs]) were conducted using a mobile laboratory in the Sujungsan Tunnel in Busan, Korea. Mobile measurements generated concentration profiles of the target exhaust emissions inside the tunnel. These data were used to produce a zonation of the tunnel, i.e., mixing and accumulation zones. There were differences between the CO2, SO2, and NOX profiles, and a starting point that was free from ambient air mixing effects could be set at 600 m from the tunnel entrance. The EFs of vehicle exhaust emissions were calculated using pollutant concentration gradients. The average EFs for CO2, NO, NO2, SO2, PM10, PM2.5, and ∑VOCs were 149,000, 380, 55, 29.2, 9.64, 4.33, and 16.7 mg km−1·veh−1, respectively. Among the VOC groups, alkanes contributed more than 70% of the VOC EF. Mobile measurement-derived EFs were validated using the conventional EFs from stationary measurements. The EF results from the mobile measurements matched those from the stationary measurements, while the absolute concentration differences between them implied complex aerodynamic movements of the target pollutants inside the tunnel. This study demonstrated the usefulness and advantages of applying mobile measurements in a tunnel environment and indicated the potential of the approach for observation-based policymaking.

DQ-MAN: A tool for multi-dimensional data quality analysis in IoT-based air quality monitoring systems

Air quality monitoring has traditionally been performed using robust specialized systems based on an air filter. These systems provide high quality data, but entail a high investment, thus limiting the scale of the deployment. An alternative way of measuring air pollution is the use of optical sensors, which are mounted on an embedded system, leading to a lower cost, as compared to the traditional solution. While these systems allow for a wider deployment at a lower cost, there is a concern on the quality of the data provided by them. In this context, the analysis of Data Quality (DQ) takes special relevance, in order to meet the requirements established by environmental agencies. In order to tackle this issue, this paper proposes a multi-dimensional model that estimates a unified DQ index, based on the integration of the relevant DQ dimensions and the subjective preferences of experts in the field. We present the development of DQ-MAN, a tool that allows the end-user to assess and visualize the DQ metrics over different time frames, and to compute the corresponding DQ index. Our tool allows the user to publish the summarized results in a web report. We validate DQ-MAN using a synthetic dataset to assess the correctness of our tool, as well as a real dataset of a low-cost monitoring system deployed in Medellín, Colombia. Based on the evaluation, we conclude that DQ-MAN is aware of changes in DQ, and how each dimension affects the overall DQ assessment.

Night light polarization: Modeling and observations of light pollution in the presence of aerosols and background skylight or airglow

We introduce a new polarized radiative transfer model able to compute the polarization measured by a virtual instrument in a given nocturnal environment recreating real world conditions (1-dimensional atmospheric and aerosol profiles, 3-dimensional light sources with complex and widespread geometries, terrain obstructions). Initially developed to address the issue of aurorae and nightglow polarization, the model has potential applications in the context of light pollution, or aerosols and air pollution measurements in night time conditions. We provide the physical assumptions behind the model together with the main points regarding its numerical implementation, together with the inherent constraints and liberties it brings. The model, based on single scattering equations in the atmosphere, is first tested on a few simple configurations to assess the effect of several key parameters in controlled environments. The model outputs are then compared to field measurements obtained in four wavelengths at mid-latitude in a dark valley of the French Alps, 20 km away from the closest city. In this context where the nightglow emissions are supposedly stationary and widespread, a convincing fit between the model predictions and observations is found in three wavelengths. This confrontation of ground-based records with our modeling constitutes a proof of concept for the investigation of our polarized environment in nocturnal conditions, in the presence of localized and/or extended sources. It calls for further investigations. In particular we discuss the future need for inter-calibrating the sources and the polarimeter in order to optimally extract the information contained in such measurements, and how multiple-scattering (not implemented in the present study) could impact our observations and their interpretation.

Integrated Assessment Modelling of Future Air Quality in the UK to 2050 and Synergies with Net-Zero Strategies

Integrated assessment modelling (IAM) has been successfully used in the development of international agreements to reduce transboundary pollution in Europe, based on the GAINS model of IIASA. At a national level in the UK, a similar approach has been taken with the UK Integrated Assessment Model, UKIAM, superimposing pollution abatement measures and behavioural change on energy projections designed to meet targets set for the reduction of greenhouse gas emissions and allowing for natural and imported contributions from other countries and shipping. This paper describes how the UKIAM was used in the development of proposed targets for the reduction of fine particulate PM2.5 in the UK Environment Act, exploring scenarios encompassing different levels of ambition in reducing the emissions of air pollutants up to 2050, with associated health and other environmental benefits. There are two PM2.5 targets, an annual mean concentration target setting a maximum concentration to be reached by a future year, and a population exposure reduction target with benefits for health across the whole population. The work goes further, also demonstrating links to social deprivation. There is a strong connection between climate measures aimed at reducing net GHG emissions to zero by 2050 and future air quality, which may be positive or negative, as illustrated by sectoral studies for road transport where electrification of the fleet needs to match the evolution of energy production, and for domestic heating, where the use of wood for heating is an air quality issue. The UKIAM has been validated against air pollution measurements and other types of modelling, but there are many uncertainties, including future energy projections.

Does It Measure Up? A Comparison of Pollution Exposure Assessment Techniques Applied across Hospitals in England

Weighted averages of air pollution measurements from monitoring stations are commonly assigned as air pollution exposures to specific locations. However, monitoring networks are spatially sparse and fail to adequately capture the spatial variability. This may introduce bias and exposure misclassification. Advanced methods of exposure assessment are rarely practicable in estimating daily concentrations over large geographical areas. We propose an accessible method using temporally adjusted land use regression models (daily LUR). We applied this to produce daily concentration estimates for nitrogen dioxide, ozone, and particulate matter in a healthcare setting across England and compared them against geographically extrapolated measurements (inverse distance weighting) from air pollution monitors. The daily LUR estimates outperformed IDW. The precision gains varied across air pollutants, suggesting that, for nitrogen dioxide and particulate matter, the health effects may be underestimated. The results emphasised the importance of spatial heterogeneity in investigating the societal impacts of air pollution, illustrating improvements achievable at a lower computational cost.

Urban landscape and climate affect residents’ sentiments based on big data

Residents’ sentiments are a quantitative indicator of human feelings, which is useful for optimizing urban residential environments. Little is known about the spatiotemporal variations and potential drivers of sentiment based on big data. A total of 221,104 Weibo social media data were used to quantify daily sentiment in the Beijing metropolitan area during the COVID-19 pandemic from January 1, 2021 to March 8, 2022. Deep-learning natural language processing was used to extract this dataset to investigate the spatiotemporal sentiment patterns. The density of roads and buildings, normalized difference vegetation index (NDVI), population, sky visibility factor, daily land surface temperature (LST), daily precipitation, and daily air pollution concentrations (CO, NO2, PM2.5, SO2, and O3) were explored as potential drivers of sentiment. Results show that (1) the holiday sentiment was 1.31% higher than on weekends and 4.61% higher than on weekdays. Extreme precipitation, air pollution, and COVID-19 lockdown measures have reduced sentiment. (2) The sentiment in spring was found to be the highest. The numbers of functional zones with high sentiment values (>0.8) in spring were 13.59%, in summer 34.48%, and in autumn 14.71%. (3) Sentiment was highest under conditions of moderate greenness (0.4<NDVI<0.6) and comfortable daily temperature (25 °C < LST<30 °C). (4) Sentiment was negatively associated with daily air pollutants, such as PM2.5, NO2, and CO. This paper presents the effectiveness of sentiment quantification based on social media data and deep-learning techniques. The results provide practical implications and support decisions for sustainable urban health development.

Association between short-term exposure to environmental air pollution and atopic dermatitis flare in patients treated with dupilumab

BackgroundThe magnitude of short/medium-term air pollution exposure on atopic dermatitis (AD) flare has not been fully investigated. The aim of the study was to investigate the association of short/medium-term exposure to airborne pollution on AD flares in patients treated with dupilumab. MethodsObservational case-crossover study. Patients with moderate-to-severe AD under treatment with dupilumab were included. The exposure of interest was the mean concentrations of coarse and fine particulate matter (PM10, PM2.5), nitrogen dioxide, and oxides (NO2, NOx). Different intervals were considered at 1 to 60 days before the AD flare and control visit, defined as the visit with the highest Eczema Area and Severity Index scores >8 and ≤7, respectively. A conditional logistic regression analysis adjusted for systemic treatments was employed to estimate the incremental odds (%) of flare every 10 μg/m3 pollutant concentration. ResultsData on 169 of 528 patients with AD having 1130 follow-up visits and 5840 air pollutant concentration measurements were retrieved. The mean age was 41.4 ± 20.3 years; 94 (55%) men. The incremental odds curve indicated a significant positive trend of AD flare for all pollutants in all time windows. At 60 days, every 10 μg/m3 PM10, PM2.5, NOx, and NO2 increase concentration was associated with 82%, 67%, 28%, and 113% odds of flare, respectively. ConclusionsIn patients treated with dupilumab, acute air pollution exposure is associated with an increased risk for AD flare with a dose-response relationship.

Bayes-based air pollution computer measurement and big data outlier detection model

In today’s rapidly changing social environment, high-dimensional data abound, but not all data are meaningful. In order to reduce costs and reduce consumption, people are more interested in some important variables. Therefore, variable selection of high-dimensional data is an important research direction. Based on the Bayesian method, it stands out among many variable selection methods with effective estimation efficiency and flexible mechanism, and it is also suitable for parametric and non-parametric models. Thanks to the flexibility of the statistical conclusion inference process, combined with prior information, the actual results are more accurate. At present, many indoor pollution problems, including decoration pollution, are getting more and more attention, and its air pollution calculations are obviously very important. Indoor air quality not only directly affects the comfort of the human body, but also significantly affects the work efficiency of family members. As a result of the development of the new digital economy, digital mining technology based on big data has brought unprecedented new development methods to various fields. This paper establishes an outlier detection model, identifies data processes that are significantly different from the normal data set or expected value, and cleans the data to improve the accuracy of the integrated detection model. This paper proposes a solution to this problem by combining big data mining technology with the establishment of an outlier detection model. At the same time, it optimizes the design of the model to reduce errors, so as to realize its accurate application in the measurement of air pollutants, and aims to promote its development.

Assessing neuropsychological disorders affecting pottery workers occupationally exposed to air pollutants

ObjectivePottery-related activities are characterized by the emission of multiple air pollutants in the form of particulate matter, gases, and organic compounds. These pollutants are associated with adverse neuropsychological effects. This study aimed at investigating the effect of occupational exposure to air pollutants on the neuropsychiatric health. MethodsA total of 180 male workers (90 exposed workers and 90 administrative employees) were recruited from pottery-making activities in the Fawakher region located in old Cairo (Misr Al-Kadema); the administrative employees were the control group. Personal, medical, and family histories, general and neurological clinical examination, and neuropsychological assessments were recorded. Serum levels of 4-hydroxy-2-nonenal levels (4HNE) were measured by ELISA. Environmental measurement of workplace air pollutants was performed. ResultsEnvironmental monitoring of the workplace revealed that workers are exposed to high levels of SO2 and NO2 as these exceeded the national standard levels. Compared to the control group, the exposed workers’ group demonstrated a significant decrease in digit forwards score, digit backward score, and symbol digit score and a significant higher Hamilton Depression Scale score, and Benton Visual Retention score. The level of 4HNE was significantly increased among the exposed workers’ group compared to that of the control group. ConclusionOccupational exposure to air pollutants is associated with impairment in neuropsychological functions, with a corresponding increase in the serum level of 4HNE, which is a biomarker for oxidative stress among Egyptian pottery workers.

Mapping Changes in Particulate Pollution and Plant Health in the Indian Sub-continent during the COVID-19 Pandemic

Particulate matter (PM) air pollution is ranked the 13^th leading cause of mortality across the globe. Lockdowns during the COVID-19 pandemic provided a unique opportunity to assess the potential beneficial impact on air quality and possibly biologic outcomes. The main objectives of this project were to utilize NASA satellite-derived data and: 1) Observe changes in PM_2.5 across the four countries before the outbreak of COVID-19 and through the observed case peak months in 2020 and 2021; 2) Examine changes in normalized difference vegetation index (NDVI) during, around, and subsequent to COVID-19 peak months; 3) Evaluate changes in precipitation and land surface temperature as other potential contributors to changes in plant health. Remote sensing datasets included "Aerosol Optical Thickness" to measure air pollution, and "Normalized Difference Vegetation Index" to examine vegetation health. We found that PM_2.5 concentration substantially decreased in some areas of the sub-continent, during the peak months, while NDVI improved. While accompanying precipitation and land surface temperature may account for some of the changes in NDVI, they alone cannot explain the improvement in plant health during shutdowns. Thus, at least some of the decreased plant stress may be attributed to lower emission of atmospheric pollutants, including PM_2.5.

How does foreign direct investment improve urban air quality?

Foreign direct investment and environmental pollution have been a prominent topic in China's economic development. Therefore, this paper investigates the relationship between foreign direct investment and environmental pollution, and makes practical policy recommendations to support the harmonious development of economy and environment based on the study findings. In this paper, the spatial econometric approach framework with the spatial weight matrix of the Delaunay method is used to examine the influence of foreign direct investment on the level of urban air quality. It uses air quality index as an indicator to measure air pollution comprehensively. Based on the spatial autocorrelation with the aggregation and radiation effects of foreign direct investment, the results show that the distribution of air pollution has considerable spatial impact in terms of spatial nonequilibrium. Through the empirical analysis, our finding reveals that the increase in GDP, the share of secondary industry, and population growth are the important elements affecting the change in air quality.

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information.

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.

The influence of anthropogenic emissions on air quality in Beijing-Tianjin-Hebei of China around 2050 under the future climate scenario

The Beijing-Tianjin-Hebei (BTH) region of China is one of the most frequently referenced areas with PM2.5 air pollution and various control measures; hence, it is critical to project the future air quality on this region under different emission scenarios. In this study, using a global climate model, regional meteorological model, emission process model and air quality model, a future air quality research modeling system is designed and utilized to explore the air quality conditions over the BTH region in 2050. Two sensitivity experiments with different intensity emission inventories were set to quantify the influence of anthropogenic emissions on the future air quality in the BTH region under the identical representative concentration pathway (RCP) scenarios of RCP4.5, which is provided by the famous climate model Geophysical Fluid Dynamics Laboratory Earth System Model with the Generalized Ocean Layer Dynamics component (GFDL-ESM2G). The results show that the annual PM2.5 concentration over BTH under the EIT1 (Emissions for Interim Target-1) scenario is 32.7 μg/m3, less than 35 μg/m3, approximately half of the value under the baseline emission scenario, and reaches the Interim Target-1 by the World Health Organization (WHO), which is also the national air quality standard for annual PM2.5 concentrations in China. The frequency of nonpolluted projects can reach 95%, and the frequency of heavily polluted projects is well under 1% under the EIT1 emission scenario. These results indicate that emission reduction is significantly required to achieve the new Chinese PM2.5 target.

Development of Autonomous Hexacopter UAVs for Smart City Air Quality Management

Monitoring the pollution index in smart cities has piqued the interest of researchers in designing and developing unmanned aerial vehicles (UAV) capable of carrying several sensors. Recent advancements in drone technology, as well as rapid expansion in air pollution sensor technologies, have presented valuable alternatives for air quality monitoring and management in smart cities. Fixed stations are now used in smart cities to measure air pollution and collect precise data on air quality. However, such data is highly sufficient in making decisions that can improve people lives; monitoring stations require an adaptable and large communication network that is capable to handle such huge data. Instead of having such an expensive and complex network, drones could be considered an easy and cheap alternative to the current systems. In this regard, an aerial system that is equipped with off-the-shelf low-cost micro-sensors is designed and implemented to monitor air quality at a specific location inside a smart city. The behavior of the aerial system is controlled by our proposed Air Quality-Driven Control Algorithm (AQDCA). Hexacopter Drone, in particular, will fly up to a predetermined height, measure air pollutants, activate the on-board AQDCA, and then return to its ground location. The entire system was developed, implemented, and tested in a real-world flight test. The testing results corroborate the system’s practicality and demonstrate that the prototype may be simply implemented to provide an added-value service to smart city citizens.