Pollution Measurements Research Articles

Engineering MOF-derived 2D NPC-TiO2 nanotablet/3D marigold-like ZnIn2S4/CdS nanoparticles dual Z-scheme heterojunction with excellent photoelectric performance: A novel material for sensitive and facile photoelectrochemical sensing detection of mercury ions in aqueous environments by ion-exchange sensing strategy

Herein, a new NH2-MIL-125-derived 2D nitrogen-doped porous carbon-titanium dioxide (NPC-TiO2) nanotablet/3D marigold-like ZnIn2S4/CdS nanoparticles (NPs) dual Z-scheme heterojunction was first exploited for photoelectrochemical (PEC) measurement of mercury ions (Hg2+). Taking NH2-MIL-125 as precursor, the 2D NPC-TiO2 nanotablet was prepared by calcination. The 3D marigold-like ZnIn2S4 and CdS NPs were synthesized by hydrothermal method and aqueous synthesis way, respectively. The acquired NPC-TiO2, ZnIn2S4 and CdS were sequentially modified on the indium tin oxide (ITO) electrode to form the PEC sensing interface by layer-by-layer assembly. Due to the matched band-edge levels, and strong light-harvesting ability, the given heterostructure on ITO electrode generated a large photocurrent. Importantly, it was found that 2D NPC-TiO2 nanotablet/3D marigold-like ZnIn2S4/CdS NPs dual Z-scheme heterojunction could show specifically increased photocurrent signal towards to Hg2+ by employing CdS NPs as Hg2+-recognition probe with selective Cd-to-Hg ion-exchange, resulting from the in-situ formation of the new quaternary NPC-TiO2/ZnIn2S4/CdS/HgS heterostructure to further facilitate the spatial transmission and separation of charges. As a result, Hg2+ was highly sensitively detected with a low detection limit of 20 fM. This PEC sensor was also verified by evaluating Hg2+ in tap water, river water and lake water, demonstrating great application prospect for environment pollutant measurement.

The Mist of corporate innovation

This study investigates the impact of air pollution on corporate innovation. Using the satellite generated Particulate Matter 2.5 concentration data in 390 cities in China over the period of 2000–2015, we find that severe air pollution (haze pollution) in a region may deteriorate the innovative performance of companies in the same region. Our results are robust to different air pollution and innovation measures. We employ both difference in differences and instrumental variable approaches to alleviate potential endogeneity concerns. In addition, we identify two possible mechanisms, reduced funds availability and loss of innovative talents, to explain the negative impact of air pollution on innovation.

Indoor and outdoor air quality in street corner kiosks in a large metropolitan area

Poor air quality in workplaces constitutes a great concern on human health as a good fraction of our time is spent at work. In Greece, very unique workplaces are the street corner kiosks, which are freestanding boxes placed on sidewalks next to city streets and vehicular traffic, where one can find many consumer goods. As such, its employees are exposed to both outdoor and indoor air pollutants. Very few studies have examined the occupational exposure of kiosk workers to air pollutants, and thus the magnitude of this unique indoor and outdoor exposure remains unknown. The objective of this study is to investigate and compare the levels of indoor and outdoor particulate matter (PM10 and PM2.5), ultrafine particles (UFPs) and black carbon (BC) in different kiosks located in Athens, Greece, in urban-traffic and urban-background environments. Continuous measurements of the above-mentioned pollutants were carried out on a 24-h basis over 7 consecutive days at three kiosks from September to October 2019. Indoor PM10 concentrations in the urban kiosk ranged from 19.0 to 44.0 μg/m3, PM2.5 values ranged from 14.0 to 33.0 μg/m3, whereas BC concentrations ranged from 1.2 to 7.0 μg/m3 and UFPs from almost 9.5 to 47.0 × 103 pt/cm3. Outdoor PM10 and PM2.5 measurements ranged from 29.0 to 59.0 μg/m3 and from 22.0 to 39.0 μg/m3, respectively. BC outdoor concentrations ranged from 1.1 to 2.2 μg/m3. The mean hazard quotient (HQ) for PM10 (4.9) and PM2.5 (4.7) among all participants was >1. The health risk of exposure to PM10 and PM2.5 was found to be at moderate hazard levels, although in some cases we observed HQ values higher than 10 due to high PM10 and PM2.5 concentrations in the kiosks. Overall our study indicates that people working at kiosks can be exposed to very high concentrations on particulate pollution depending on a number of factors including the traffic that strongly depends on location and the time of the day.

Street-scale air quality modelling over the West Midlands, United Kingdom: Effect of idealised traffic reduction scenarios

Air pollution is the major environmental risk to human health. Road transport is one of the major sources for air pollution, particularly nitrogen dioxide, in urban areas, and hence traffic control is an important measure in air quality management. A street-scale air quality model, ADMS-Urban, was configured for a case study of the West Midlands, UK to represent a baseline year (2019). Model outputs were evaluated using hourly air pollutant measurement data, and the model demonstrates good performance overall. This modelling tool was then used to explore the effect of five hypothetical traffic reduction scenarios, ranging from 10% to 90% reduction in traffic activity; scenario impacts were analysed over a range of spatial resolutions. The impacts of traffic reduction are highly dependent on spatial resolution (i.e. street scale, electoral ward level and local authority level), which has to be taken into account when formulating policies for managing air quality on local and city-wide scales. There was an almost linear relationship between the predicted annual concentration and traffic reduction for both NO2 and PM2.5. Traffic reduction would principally reduce NO2 concentrations, with even very substantial changes in traffic having more limited effects on reducing PM2.5 concentrations reflecting the importance of regional and non-traffic PM2.5 sources.

Analysis of air quality and ventilation systems in production working areas of PT. A

PT. A is a manufacturing industry specializing in plastic-based cosmetic packaging. The work area of blow molding and injection molding 1 & 2 is the center of the company's main activity, involving many employees with diverse roles. The high density of work activity and heat-generating machines pose significant challenges to the workspace temperature, impacting worker health and well-being. Indoor air pollution levels, 2 to 10 times higher than outside, raise serious concerns, given people's prolonged indoor exposure. Poor air quality can lead to discomfort, health issues, and work accidents, affecting company operations and customer confidence. Three production units of PT. A were analyzed, focusing on air quality and ventilation systems. Primary data collection involved observations and interviews with four company informants regarding the ventilation system. Secondary data included direct measurements of air pollutants, dust particles, and environmental conditions by designated third parties. PT. A has implemented multiple mechanical ventilation systems to enhance airflow within the production area. Although the air quality in the blow molding and injection molding 1&2 workspaces meets the standards of the Minister of Manpower of the Republic of Indonesia No. 5 of 2018, the working climate in these three production areas exceeds the specified thresholds. Engineering interventions are recommended to address this issue and improve worker safety, health, comfort, and productivity without long-term consequences.

Air Quality Class Prediction Using Machine Learning Methods Based on Monitoring Data and Secondary Modeling

Addressing the constraints inherent in traditional primary Air Quality Index (AQI) forecasting models and the shortcomings in the exploitation of meteorological data, this research introduces a novel air quality prediction methodology leveraging machine learning and the enhanced modeling of secondary data. The dataset employed encompasses forecast data on primary pollutant concentrations and primary meteorological conditions, alongside actual meteorological observations and pollutant concentration measurements, spanning from 23 July 2020 to 13 July 2021, sourced from long-term air quality projections at various monitoring stations within Jinan, China. Initially, through a rigorous correlation analysis, ten meteorological factors were selected, comprising both measured and forecasted data across five categories each. Subsequently, the significance of these ten factors was assessed and ranked based on their impact on different pollutant concentrations, utilizing a combination of univariate and multivariate significance analyses alongside a random forest approach. Seasonal characteristic analysis highlighted the distinct seasonal impacts of temperature, humidity, air pressure, and general atmospheric conditions on the concentrations of six key air pollutants. The performance evaluation of various machine learning-based classification prediction models revealed the Light Gradient Boosting Machine (LightGBM) classifier as the most effective, achieving an accuracy rate of 97.5% and an F1 score of 93.3%. Furthermore, experimental results for AQI prediction indicated the Long Short-Term Memory (LSTM) model as superior, demonstrating a goodness-of-fit of 91.37% for AQI predictions, 90.46% for O3 predictions, and a perfect fit for the primary pollutant test set. Collectively, these findings affirm the reliability and efficacy of the employed machine learning models in air quality forecasting.

Variability and health impact of air pollutants and bioaerosols in multi-functional indoor environments with mechanical ventilation system

The condition of indoor environments significantly impacts human daily life and health, where exposure to harmful substances affects the resilience of occupants. To understand the dynamics of indoor air quality and its implications for occupant health thus this study evaluated air pollutant concentration (TVOC, NO2, O3, CO, CO2, PM10, PM2.5, and PM1) and bioaerosol in mechanically ventilated offices, laboratories, corridors, a library and a mechanical workshop. Simultaneous measurements of all air pollutants were performed for 24 h, including non-working hours, while bioaerosol was sampled for fungal identification and quantified via the colony counting method. High concentrations of PM10, PM2.5, PM1, O3 and bioaerosol were found in the mechanical workshop, where these pollutants were strongly related to humidity (p < 0.05) and indoor source contributions. Other air pollutants such as TVOC had the highest concentrations in the office areas during working hours with an average concentration of 361 ± 14.8 μg m−3, while for CO, the highest concentration was observed in the laboratories for both working hours (442 ± 26.0 ppb) and 24 h average concentration (438 ± 22.1 ppb). Dominant species identified in bioaerosol were Aspergillus sp., Geotrichum sp., Fusarium sp., and Penicillium sp., where the exposure to bioaerosol was found to be of low risk to all workers as the hazard quotient (HQ) was <1 for all multi-functional indoor environments. The results from this study suggest that indoor air pollutants and bioaerosol concentrations have a variety of characteristics and source contributions, especially from human activities and indoor sources.

Quantifying the impact: Are coastal areas impoverished by marine pollution?

We propose a methodological framework to assess the causal impact of Harmful Algal Bloom (HAB) events on economic indicators at a territorial level, with special consideration for spatial effects. Using the Mar Menor region in Spain as a case study, we empirically apply our framework. Our findings indicate a significantly negative causal effect of marine pollution resulting from HAB events on income per capita at the census section level. We observe that this effect is exacerbated by spatial interactions among neighboring census sections adjacent to those directly affected by seawater degradation. These results underscore the importance of implementing effective environmental regulations to mitigate seawater pollution and proactive measures to safeguard the well-being of local populations. Our research provides valuable insights for future studies in similar coastal regions.

CO anthropogenic emissions in Europe from 2011 to 2021: insights from Measurement of Pollution in the Troposphere (MOPITT) satellite data

CO anthropogenic emissions in Europe from 2011 to 2021: insights from Measurement of Pollution in the Troposphere (MOPITT) satellite data

Decoupling Agricultural Grey Water Footprint from Economic Growth in the Yellow River Basin

Decoupling agricultural economic growth from agricultural water pollution is of great importance to regional sustainable development. It is necessary to further explore the decoupling state and key driving factors connecting agricultural water pollution and agricultural economic growth on the basis of accurate measurement of agricultural water pollution. Accordingly, taking the Yellow River Basin (YRB) as the research object, this study combined the water footprint theory, the Logarithmic Mean Divisia Index (LMDI) model and the Tapio decoupling model (TDM) to conduct an in-depth decoupling analysis of the connection between the agricultural grey water footprint (AGWF) and agricultural economic growth in the YRB. Specifically, this study first calculated the AGWF of the YRB during 2016–2021 and objectively evaluated the water resource utilization in this region based on the AGWF. Then, the LMDI model was used to explore the driving factors of the AGWF in the YRB. Finally, the decoupling states between the AGWF and its driving factors with agricultural GDP (AGDP) were studied using the TDM. The main results are as follows: (1) The overall AGWF in the YRB showed a decreasing trend and a slow increase, decreasing by 5.39% in 2021 compared to 2016. (2) The primary promoting factor and inhibiting factor of AGWF reduction are the efficiency effect and agricultural economic effect, respectively. (3) The decoupling states of the AGWF and AGDP presented strong decoupling (SD) and then weak decoupling (WD) in the YRB during the research period. The decoupling states between the agricultural grey water footprint intensity (AGWFI) and AGDP changed from expansive negative decoupling (END) to SD. The decoupling state of population and AGDP remained SD. This study will contribute to alleviating agricultural water pollution in the YRB and help policymakers in water-stressed countries to formulate agricultural water management policies.

Under the dome: Air pollution and analyst forecast optimism

In this study, we investigate whether unpleasant mood caused by air pollution leads to lower analyst forecast optimism. Using a sample of 103,768 observations from 2014 to 2021, we show that there is a negative association between air pollution and analyst forecast optimism. Furthermore, we find that analysts become more optimistic forecasters after moving to a city with lower level of air pollution. Our results hold through various robustness tests including alternative measures of air pollution, controlling for various fixed effects, 2SLS by instrumenting for air pollution using thermal inversions and a placebo test by randomizing the headquarter locations of brokerage firms in our sample. Our paper provides plausibly causal evidence that air pollution lowers analyst forecast optimism.

Identifying episodic carbon monoxide emission events in the MOPITT measurement dataset

Abstract. The Measurements Of Pollution In The Troposphere (MOPITT) instrument aboard NASA's Terra satellite has been measuring upwelling radiance in a nadir-viewing mode since March 2000. These radiance measurements are inverted to yield estimates of carbon monoxide (CO) profiles and total columns, providing the longest satellite record of this trace gas to date. The CO measurements from MOPITT have been used in a variety of ways, including trend analyses and the construction of CO budgets. However, their use is complicated by the influence of episodic emission events, which release large quantities of CO into the atmosphere with irregular timing, such as large sporadic wildfires of natural or anthropogenic origin. The chaotic nature of these events is a large source of variability in CO budgets and models, requiring that these events be well characterized in order to develop an improved understanding of the role they have in influencing tropospheric CO. This study describes the development of a multistep algorithm that is used to identify large episodic emission events using daily mean Level 2 (L2) MOPITT total column measurements gridded to a 0.5 by 0.5° spatial resolution. The core component of this procedure involves empirically determining the expectation density function (EDF) that describes the departure of daily-mean CO observations from the baseline behaviour of CO, as described by its periodic components and trends. The EDFs employed are not assumed to be symmetric but instead are constructed from a pair of superimposed normal distributions. Enhancement flag files are produced following this methodology, identifying the episodic events that show strong enhancement of CO outside of the range of expected CO behaviour and are now made available for the period 3 March 2000 to 31 July 2022. The distribution and frequency of these flagged measurements over this 22-year period are analyzed in order to illustrate the robustness of this method.

Advantages of assimilating multispectral satellite retrievals of atmospheric composition: a demonstration using MOPITT carbon monoxide products

Abstract. The Measurements Of Pollution In The Troposphere (MOPITT) is an ideal instrument to understand the impact of (1) assimilating multispectral and joint retrievals versus single spectral products, (2) assimilating satellite profile products versus column products, and (3) assimilating multispectral and joint retrievals versus assimilating individual products separately. We use the Community Atmosphere Model with chemistry with the Data Assimilation Research Testbed (CAM-chem+DART) to assimilate different MOPITT carbon monoxide (CO) products to address these three questions. Both anthropogenic and fire CO emissions are optimized in the data assimilation experiments. The results are compared with independent CO observations from TROPOspheric Monitoring Instrument (TROPOMI), the Total Carbon Column Observing Network (TCCON), NOAA Carbon Cycle Greenhouse Gases (CCGG) sites, In-service Aircraft for a Global Observing System (IAGOS), and Western wildfire Experiment for Cloud chemistry, Aerosol absorption and Nitrogen (WE-CAN). We find that (1) assimilating the MOPITT joint (multispectral; near-IR and thermal IR) column product leads to better model–observation agreement at and near the surface than assimilating the MOPITT thermal-IR-only column retrieval. (2) Assimilating column products has a larger impact and improvement for background and large-scale CO compared to assimilating profile products due to vertical localization in profile assimilation. However, profile assimilation can outperform column assimilations in fire-impacted regions and near the surface. (3) Assimilating multispectral and joint products results in similar or slightly better agreement with observations compared to assimilating the single spectral products separately.

МЕТОДОЛОГИЯ ПРИНЯТИЯ ОПТИМАЛЬНЫХ РЕШЕНИЙ ПО УПРАВЛЕНИЮ ЖИЗНЕННЫМ ЦИКЛОМ ОБЪЕКТА КАПИТАЛЬНОГО СТРОИТЕЛЬСТВА С УЧЕТОМ КРИТЕРИЯ ПЫЛЕВОГО ЗАГРЯЗНЕНИЯ АТМОСФЕРНОГО ВОЗДУХА

Abstract. The existing approaches to decision-making in construction have two significant drawbacks. Firstly, they allow us to calculate the effectiveness of the options under consideration only for the object construction phase, without giving an idea of the decisions effectiveness taken at other life cycle stages. Secondly, the absence of an environmental safety criterion for a particular technology in the model reduces its universality and the adequacy of the decisions taken.&#x0D; The article proposes a new approach to management decision-making in construction as to the management of a capital construction object life cycle. A methodology has been developed for making optimal decisions on life cycle management, taking into account the criterion of atmospheric air dust pollution. In order to build the methodology, a block diagram of the formation of dust emissions into the atmosphere from temporary pollution sources at various stages was developed. The dust pollution values for works performed at various life cycle stages of a capital construction object were obtained experimentally. Based on the data obtained, the values of the given optimization criterion are determined for choosing the optimal solution for life cycle management, taking into account the criterion of atmospheric air dust pollution.&#x0D; Unlike the existing ones, the developed methodology allows, firstly, to calculate the effectiveness of the options under consideration not only for the construction stage, but also for the entire object life cycle. Secondly, the model introduces a criterion of environmental safety of the control solutions under consideration, quantified by the degree of dust pollution of atmospheric air.&#x0D; Subject of research: The processes of atmospheric air dust pollution when performing work at various stages of the capital construction object life cycle. &#x0D; Materials and methods: Theoretical and experimental methods for determining the concentration of dust pollution using the Ecologist program, based on the results of field dust pollution measurements and aerosol dispersed composition analysis.&#x0D; Results: A methodology for making optimal decisions on managing the capital construction object life cycle has been developed, including 5 stages.&#x0D; 1. Identification of work types that cause atmospheric air dust pollution.&#x0D; 2. Determination of fine dust concentration from each type of work.&#x0D; 3. Determination of the atmospheric air pollution indicator total value for the implementation of all control solutions (technologies) variants and for all capital construction object life cycle stages according to the proposed model.&#x0D; 4. Determination of given optimization criterion values for the implementation of all control solutions (technologies) variants and for all life cycle stages according to the proposed expression.&#x0D; 5. Selection of the control solution (technology) with the lowest given optimization criterion value.&#x0D; Conclusions: As a result of theoretical and practical research, a methodology has been created that allows, firstly, to calculate the effectiveness of the options under consideration not only for the construction stage, but also for the entire object life cycle. Secondly, the model introduces a criterion of environmental safety of the control solutions under consideration, quantified by the atmospheric air dust pollution degree.

Persistent DNA Methylation Changes across the First Year of Life and Prenatal Exposure in a Canadian Prospective Birth Study.

Evidence suggests that prenatal air pollution exposure alters DNA methylation (DNAm), which could go on to affect long-term health. It remains unclear whether DNAm alterations present at birth persist through early life. Identifying persistent DNAm changes would provide greater insight into the molecular mechanisms contributing to the association of prenatal air pollution exposure with atopic diseases. This study investigated DNAm differences associated with prenatal nitrogen dioxide () exposure (a surrogate measure of traffic-related air pollution) at birth and 1 y of age and examined their role in atopic disease. We focused on regions showing persistent DNAm differences from birth to 1 y of age and regions uniquely associated with postnatal exposure. Microarrays measured DNAm at birth and at 1 y of age for an atopy-enriched subset of Canadian Health Infant Longitudinal Development (CHILD) study participants. Individual and regional DNAm differences associated with prenatal () were identified, and their persistence at age 1 y were investigated using linear mixed effects models (). Postnatal-specific DNAm differences () were isolated, and their association with in the first year of life was examined. Causal mediation investigated whether DNAm differences mediated associations between and age 1 y atopy or wheeze. Analyses were repeated using biological sex-stratified data. At birth (), 18 regions of DNAm were associated with , with several annotated to HOX genes. Some of these regions were specifically identified in males (), but not females (). The effect of prenatal across CpGs within altered regions persisted at 1 y of age. No significant mediation effects were identified. Sex-stratified analyses identified postnatal-specific DNAm alterations. Regional cord blood DNAm differences associated with prenatal persisted through at least the first year of life in CHILD participants. Some differences may represent sex-specific alterations, but replication in larger cohorts is needed. The early postnatal period remained a sensitive window to DNAm perturbations. https://doi.org/10.1289/EHP13034.

Joint effects of prenatal exposure to indoor air pollution and psychosocial factors on early life inflammation

It is hypothesized that air pollution and stress impact the central nervous system through neuroinflammatory pathways Despite this, the association between prenatal exposure to indoor air pollution and psychosocial factors on inflammatory markers in infancy has been underexplored in epidemiology studies. This study investigates the individual and joint effects of prenatal exposure to indoor air pollution and psychosocial factors on early life inflammation (interleukin-1β (IL-1β), interleukin-6 (IL-6), and tumor necrosis factor-α (TNF-α)). We analyzed data from the South African Drakenstein Child Health Study (N = 225). Indoor air pollution and psychosocial factor measurements were taken in the 2nd trimester of pregnancy. Circulating inflammatory markers (IL-1β, Il-6, and TNF-α) were measured in serum in the infants at 6 weeks postnatal. Linear regression models were used to investigate associations between individual exposures and inflammatory markers. To investigate joint effects of environmental and psychosocial factors, Self-Organizing Maps (SOM) were used to create exposure profile clusters. These clusters were added to linear regression models to investigate the associations between exposure profiles and inflammatory markers. All models were adjusted for maternal age, maternal HIV status, and ancestry to control for confounding. Most indoor air pollutants were positively associated with inflammatory markers, particularly benzene and TNF-α in single pollutant models. No consistent patterns were found for psychosocial factors in single-exposure linear regression models. In joint effects analyses, the SOM profile with high indoor air pollution, low SES, and high maternal depressive symptoms were associated with higher inflammation. Indoor air pollutants were consistently associated with increased inflammation in both individual and joint effects models, particularly in combination with low SES and maternal depressive symptoms. The trend for individual psychosocial factors was not as clear, with mainly null associations. As we have observed pro- and anti-inflammatory effects, future research should investigate joint effects of these exposures on inflammation and their health effects.

Prediction of Air Pollution with Machine Learning Algorithms

Air pollution has become an important problem due to its threats. Air pollutants are in complex interaction with atmosphere and environment. For this reason, it is important to study air pollution issues. In recent years, studies on prediction of air pollutants with machine learning methods have gained momentum. In this study, some air pollutants are predicted with various machine learning algorithms considering meteorological factors. In machine learning phase, a separate study is conducted with various machine learning algorithms (multilayer perceptron neural network, stochastic gradient descent, ridge regression, cross decomposition) considering temperature, relative humidity, wind, pressure and air pollutant measurements of previous hour. Consistencies of these algorithms in estimating pollutant concentrations are compared. Various statistical metrics are used to analyze the consistencies. As a result, the coefficient of determination of all algorithms are found above 0.67, considering the test section. It is found that the coefficient of determination of the multilayer perceptron neural network algorithm provides better results than other algorithms.

Abstract P276: De Novo Exposomic Geospatial Assembly of the Stroke Belt With Machine Learning and Network Analysis

Introduction: Geographical chronic disease patterns, such as the high stroke rates in the southeast United States that characterize the Stroke Belt, are known to vary with chronic disease rates and changes in the social determinants of health (SDOH). However, the role of regional variations in the exposome (quantified by pollution measures) is less clear. Hypothesis: We investigated the hypothesis that there is a geographical relationship between high-dimensional pollution indicators and stroke, and examined if this relationship could be identified using a novel machine-learning pipeline called aPEER (algorithm for Projection of Exposome and Epidemiological Relationships). Methods: aPEER assembles geographical regions from pollution and chronic disease data at the county and census-tract level using machine-learning, and validates findings with network analysis (Figure 1A). Multiple rounds of aPEER analyses were completed with different pollution datasets to iteratively identify the most important set of pollution measures that best predict the geographical distribution of the Stroke Belt and high stroke mortality regions (Figure 1B). Results: Using aPEER, the pollutants formaldehyde, methanol, acetaldehyde, benzene and carbon tetrachloride predicted high stroke mortality regions including the Stroke Belt with superior accuracy (area under the curve (AUC)=0.8417-0.9931) compared to SDOH (AUC=0.6853-0.8860) and chronic disease (AUC=0.8367-0.9731) indicators. Conclusion: In conclusion, we identified five pollutants associated with stroke geography using a combination of machine-learning methods comprising the aPEER pipeline. These pollutants showed superior predictive power compared to conventional SDOH and chronic disease risk factors, highlighting the potential role of the exposome in stroke pathogenesis.

Application of the principles of systems analysis for the development of environment for integrated air pollution forecasting

Air pollution forecasting is a well-known scientific problem, but nitrogen dioxide is not often considered as a pollutant in the problem. Traffic, presented by vehicle emissions, is one of the main sources of air pollution by nitrogen dioxide. Some existing investigations take traffic into account as input features for forecasting models but traffic is presented by values measured during previous hours. Therefore, separate models are created and trained for every air pollution measurement station. Trained forecasting models are not used systemati-cally and models for forecasting other indicators are not applied. But air pollution over cur-rent hour is caused by traffic mostly at the same place over the same hour but not over previ-ous hours. Therefore, it is important to investigate how usage of forecasted values of traffic impacts on air pollution by nitrogen dioxide in comparison with usage of traffic values meas-ured over previous hours. This task is considered in the paper. The objective of the study is to investigate impact of system approach on accuracy of forecasting air pollution by nitrogen dioxide. Three types of forecasting models based on long short-term memory were created to investigate such an impact. These types include models with concentration of nitrogen dioxide over previous hours as input features and two types of models with additional input features presented by values of traffic over previous hours and by forecasted values of traffic over output hours correspondingly. The last type of models pre-sented application of systems approach, as it uses other models available in the integrated forecasting environment. Experimental investigation allowed to create and train models as well as to compare the results of the models on the datasets representing car traffic and air pollution in Madrid. Application of the approach based on the principles of systems analysis increases accuracy of forecasting air pollution by nitrogen dioxide. It enables to create inte-grated air pollution forecasting environment, which combines information technologies of traffic and air pollution forecasting.

Quantitative Estimation of the Impacts of Precursor Emissions on Surface O3 and PM2.5 Collaborative Pollution in Three Typical Regions of China via Multi-Task Learning

The coordinated control of PM2.5 and O3 pollution has become a critical factor restricting the improvement of air quality in China. In this work, precursors and related influencing factors were utilized to establish PM2.5 and O3 estimation models in the North China Plain (NCP), the Yangzi River Delta (YRD), and the Pearl River Delta (PRD) using a multi-task-learning (MTL) model. The prediction accuracy of these three MTL models was high, with R2 values ranging from 0.69 to 0.83. Subsequently, these MTL models were used to quantitatively reveal the relative importance of each factor to PM2.5 and O3 collaborative pollution simultaneously. Precursors and meteorological factors were the two most critical influencing factors for PM2.5 and O3 pollution in three regions, with their relative importance values larger than 29.99% and 15.89%, respectively. Furthermore, these models were used to reveal the response of PM2.5 and O3 to each precursor in each region. In the NCP and the YRD, the two most important precursors of PM2.5 pollution are SO2 and HCHO, while the two most critical factors for O3 pollution are HCHO and NO2. Therefore, SO2 and VOC emissions reduction is the most important measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in these two regions. In terms of the PRD, SO2 and NO2 are the most important precursors of PM2.5 pollution, while the most important precursors for O3 pollution are HCHO and SOX, respectively. Thus, NO2, SO2, and VOC emission reduction is the most critical measure for PM2.5 pollution, while VOC and NO2 emission reduction is the most critical measure for O3 pollution in the PRD. Overall, this study provides clues and references for the control of PM2.5 and O3 collaborative pollution in the NCP, the YRD, and the PRD.