Association of air quality reduction with incident dementia: Effects of natural course and hypothetical air pollutant interventions using g‐computation

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

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Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Atmosphere ◽

10.3390/atmos12060788 ◽

2021 ◽

Vol 12 (6) ◽

pp. 788

Author(s):

Rong Feng ◽

Hongmei Xu ◽

Zexuan Wang ◽

Yunxuan Gu ◽

Zhe Liu ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Rural Areas ◽

Vehicle Emissions ◽

Motor Vehicle ◽

Northwest China ◽

Air Pollutant ◽

Capital City ◽

Air Pollution Control ◽

Motor Vehicle Emissions

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

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Abrupt but smaller than expected changes in surface air quality attributable to COVID-19 lockdowns

Science Advances ◽

10.1126/sciadv.abd6696 ◽

2021 ◽

Vol 7 (3) ◽

pp. eabd6696

Author(s):

Zongbo Shi ◽

Congbo Song ◽

Bowen Liu ◽

Gongda Lu ◽

Jingsha Xu ◽

...

Keyword(s):

Air Quality ◽

Air Pollutant ◽

Pollutant Emissions ◽

Quality Improvements ◽

Learning Technique ◽

Emission Changes ◽

Almost All ◽

Gaseous Oxidant ◽

Sophisticated Analysis ◽

Limited Change

The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO2 concentrations and increases in O3 were observed in almost all cities. However, the decline in NO2 concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O3 increased by 2 to 30% (except for London), the total gaseous oxidant (Ox = NO2 + O3) showed limited change, and PM2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

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Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

Journal of Clinical Medicine ◽

10.3390/jcm9082351 ◽

2020 ◽

Vol 9 (8) ◽

pp. 2351

Author(s):

Łukasz Kuźma ◽

Krzysztof Struniawski ◽

Szymon Pogorzelski ◽

Hanna Bachórzewska-Gajewska ◽

Sławomir Dobrzycki

Keyword(s):

Public Health ◽

Gender Differences ◽

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

World Health ◽

The European Union ◽

Daily Mortality ◽

Male Population ◽

The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

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Application of combined model of stepwise regression analysis and artificial neural network in data calibration of miniature air quality detector

Scientific Reports ◽

10.1038/s41598-021-82871-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Bing Liu ◽

Qingbo Zhao ◽

Yueqiang Jin ◽

Jiayu Shen ◽

Chaoyang Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Regression Model ◽

Multilayer Perceptron ◽

Stepwise Regression ◽

Control Point ◽

Measurement Data ◽

Environmental Indicators ◽

Air Pollutant ◽

Mlp Model

AbstractIn this paper, six types of air pollutant concentrations are taken as the research object, and the data monitored by the micro air quality detector are calibrated by the national control point measurement data. We use correlation analysis to find out the main factors affecting air quality, and then build a stepwise regression model for six types of pollutants based on 8 months of data. Taking the stepwise regression fitting value and the data monitored by the miniature air quality detector as input variables, combined with the multilayer perceptron neural network, the SRA-MLP model was obtained to correct the pollutant data. We compared the stepwise regression model, the standard multilayer perceptron neural network and the SRA-MLP model by three indicators. Whether it is root mean square error, average absolute error or average relative error, SRA-MLP model is the best model. Using the SRA-MLP model to correct the data can increase the accuracy of the self-built point data by 42.5% to 86.5%. The SRA-MLP model has excellent prediction effects on both the training set and the test set, indicating that it has good generalization ability. This model plays a positive role in scientific arrangement and promotion of miniature air quality detectors. It can be applied not only to air quality monitoring, but also to the monitoring of other environmental indicators.

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PENGEMBANGAN MODUL PRAKTIKUM TEMA ALAT PENGENDALI PENCEMAR UDARA BERBASIS AUGMENTED REALITY

Jurnal Teknologi Pendidikan (JTP) ◽

10.24114/jtp.v14i1.22203 ◽

2021 ◽

Vol 14 (1) ◽

pp. 11

Author(s):

Yudith Vega Paramitadevi ◽

Faldiena Marcelita ◽

Ana Turyanti

Keyword(s):

Environmental Management ◽

Virtual Reality ◽

Air Quality ◽

Augmented Reality ◽

Control Design ◽

Air Pollutant ◽

Learning Materials ◽

Study Program ◽

Management Study ◽

Constructive Analysis

Abstrak: Penciptaan dan pengembangan material pembelajaran dalam situasi ketidakpastian saat ini merupakan tantangan yang dihadapi pendidik. Tantangan tersebut terjawab seiring teknologi Augmented dan Virtual Reality (AR dan VR) mulai diterapkan di kelas praktikum. Pengembangan modul praktikum mata kuliah Kualitas Udara dan penilaian keberterimaan modul bagi mahasiswa Prodi D3 Teknik dan Manajemen Lingkungan merupakan tujuan dalam penelitian ini. Modul terdiri dari buku panduan, marker AR, aplikasi KU-AR dan kuisioner formatif yang diisi oleh responden 28 mahasiswa. Analisis formatif menunjukkan mayoritas mahasiswa dapat menerima modul dan menjadi termotivasi untuk mempelajari desain alat pengendali pencemar udara lanjutan. Pengenalan alat pengendali pencemar udara melalui modul AR dapat memberikan pengalaman yang berguna bagi mahasiswa, khususnya vokasi. Kata Kunci: alat pengendali, aplikasi KU-AR, modul praktikum, pengembanganAbstract: The creation and development of learning materials in today's uncertainty is a challenge for educators. Augmented and Virtual Reality (AR and VR) technologies responded to this challenge since they began to be applied in practicum classes. The development of the practicum module for the Air Quality course and the assessment of module acceptance for D3 Engineering and Environmental Management Study Program students are the objectives of this research. The module consists of a guidebook, AR markers, the KU-AR application, and a formative questionnaire filled out by 28 student respondents. The constructive analysis shows that most students can receive the module and become motivated to study advanced air pollutant control design. The introduction of air pollutant controllers through the AR module can provide a useful experience for students, especially vocational students.Keywords: air pollutant controller, development, KU-AR application, practicum module

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To what extent does the environmental magnetism technique acts as a way of assessing the uptake of atmospheric particles by plants?

10.5194/egusphere-egu21-8263 ◽

2021 ◽

Author(s):

Sarah Letaïef ◽

Pierre Camps ◽

Thierry Poidras ◽

Patrick Nicol ◽

Delphine Bosch ◽

...

Keyword(s):

Air Quality ◽

Endemic Species ◽

Fine Particles ◽

Magnetic Measurements ◽

Deposition Velocity ◽

Environmental Magnetism ◽

Air Pollutant ◽

Atmospheric Pollutants ◽

Area Index ◽

The Impact

Numerous studies have already shown the possibility of tracing the sources, the compositions, and the concentration of atmospheric pollutants deposited on plant leaves. In environmental geochemistry, inter-element and isotope ratios from chemical element assays have been used for these purposes. Alternatively, environmental magnetism represents a quick and inexpensive asset that is increasingly used as a relative indicator for concentrations of air pollutant on bio accumulator surfaces such as plants. However, a fundamental issue is still pending: Do plants in urban areas represent a sink for fine particles that is sufficiently effective to improve air quality? This is a very topical issue because some studies have shown that the foliage can trap fine particles by different dry deposition processes, while other studies based on CFD models indicate that plant hedges in cities can hinder the atmospheric dispersion of pollutants and therefore increase pollution at the level of emission sources such as traffic. To date, no consensus was made because several factors not necessary well known must be taken into account, such as, PM concentration and size, prevailing wind, surface structures, epicuticular wax, to mention just a few examples. A first step toward the understanding of the impact of urban greens on air quality is the precise determination of the deposition velocity (Vd) parameter. This latter is specific for each species and it is most of the time underestimated in modeling-based studies by taking standard values. In that perspective, we built a wind tunnel (6 m long, 86 cm wide and 86 cm high) to perform analogical experiments on different endemic species. All parameters are controlled, i.e, the wind speed, the nature and the injection time of pollutants (Gasoline or Diesel exhausts, brakes or tires dust, etc&#8230;). We can provide the PM concentrations upwind and downwind of natural reconstituted hedges by two dustmeters (LOACs - M&#233;t&#233;oModem). Beforehand, parameters such as the hedge resistance (%) or the leaf area index (LAI) have been estimated for each studied specie to allow comparability between plants removal potential. The interest would ultimately combine PM concentration measured by size bins from the LOACs with magnetic measurements (ARM, IRM100mT, IRM300mT and SIRM) of plant leaves. The idea is to check whether it would be possible to precisely determine in situ the dust removal rate by urban greens with environmental magnetism measurements. Up to now, we have carried out on different endemic species such as Elaeagnus x ebbingei leaves and Mediterranean pine needles, the results of which will be presented.

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Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

Atmospheric Measurement Techniques ◽

10.5194/amt-10-3575-2017 ◽

2017 ◽

Vol 10 (9) ◽

pp. 3575-3588 ◽

Cited By ~ 83

Author(s):

Eben S. Cross ◽

Leah R. Williams ◽

David K. Lewis ◽

Gregory R. Magoon ◽

Timothy B. Onasch ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Lower Cost ◽

Electrochemical Sensors ◽

Air Pollutant ◽

Sensor System ◽

Time Interval ◽

High Dimensional Model Representation ◽

Integrated Sensor ◽

Mean Square Errors

Abstract. The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC) sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO]  =  231 ± 116 ppb (spanning 84–1706 ppb), [NO]  =  6.1 ± 11.5 ppb (spanning 0–209 ppb), [NO2]  =  11.7 ± 8.3 ppb (spanning 0–71 ppb), and [O3]  =  23.2 ± 12.5 ppb (spanning 0–99 ppb). Through the use of high-dimensional model representation (HDMR), we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature  =  23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity  =  50.1 ± 15.3 %, spanning 9.8–79.9 %) can be effectively modeled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root mean square errors (RMSE) of 39.2, 4.52, 4.56, and 9.71 ppb, respectively. Our results substantiate the potential for distributed air pollution measurements that could be enabled with these sensors.

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Evaluation of dust and trace metal estimates from the Community Multiscale Air Quality (CMAQ) model version 5.0

Geoscientific Model Development ◽

10.5194/gmd-6-883-2013 ◽

2013 ◽

Vol 6 (4) ◽

pp. 883-899 ◽

Cited By ~ 116

Author(s):

K. W. Appel ◽

G. A. Pouliot ◽

H. Simon ◽

G. Sarwar ◽

H. O. T. Pye ◽

...

Keyword(s):

United States ◽

Air Quality ◽

Trace Metals ◽

Urban Areas ◽

Temporal Distribution ◽

Air Pollutant ◽

Eastern United States ◽

Air Quality Model ◽

Quality Model ◽

Model Version

Abstract. The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science air quality model that simulates the emission, transformation, transport, and fate of the many different air pollutant species that comprise particulate matter (PM), including dust (or soil). The CMAQ model version 5.0 (CMAQv5.0) has several enhancements over the previous version of the model for estimating the emission and transport of dust, including the ability to track the specific elemental constituents of dust and have the model-derived concentrations of those elements participate in chemistry. The latest version of the model also includes a parameterization to estimate emissions of dust due to wind action. The CMAQv5.0 modeling system was used to simulate the entire year 2006 for the continental United States, and the model estimates were evaluated against daily surface-based measurements from several air quality networks. The CMAQ modeling system overall did well replicating the observed soil concentrations in the western United States (mean bias generally around ±0.5 μg m−3); however, the model consistently overestimated the observed soil concentrations in the eastern United States (mean bias generally between 0.5–1.5 μg m−3), regardless of season. The performance of the individual trace metals was highly dependent on the network, species, and season, with relatively small biases for Fe, Al, Si, and Ti throughout the year at the Interagency Monitoring of Protected Visual Environments (IMPROVE) sites, while Ca, K, and Mn were overestimated and Mg underestimated. For the urban Chemical Speciation Network (CSN) sites, Fe, Mg, and Mn, while overestimated, had comparatively better performance throughout the year than the other trace metals, which were consistently overestimated, including very large overestimations of Al (380%), Ti (370%) and Si (470%) in the fall. An underestimation of nighttime mixing in the urban areas appears to contribute to the overestimation of trace metals. Removing the anthropogenic fugitive dust (AFD) emissions and the effects of wind-blown dust (WBD) lowered the model soil concentrations. However, even with both AFD emissions and WBD effects removed, soil concentrations were still often overestimated, suggesting that there are other sources of errors in the modeling system that contribute to the overestimation of soil components. Efforts are underway to improve both the nighttime mixing in urban areas and the spatial and temporal distribution of dust-related emission sources in the emissions inventory.

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Regional Energy, CO2, and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach

Energies ◽

10.3390/en11082119 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2119 ◽

Cited By ~ 3

Author(s):

Ying Li ◽

Yung-Ho Chiu ◽

Liang Lu

Keyword(s):

Particulate Matter ◽

Energy Consumption ◽

Air Quality ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Significant Difference ◽

Desirable Output ◽

Reduce Emissions ◽

Carbon Dioxide Co2

Rapid economic development has resulted in a significant increase in energy consumption and pollution such as carbon dioxide (CO2), particulate matter (PM2.5), particulate matter 10 (PM10), SO2, and NO2 emissions, which can cause cardiovascular and respiratory diseases. Therefore, to ensure a sustainable future, it is essential to improve economic efficiency and reduce emissions. Using a Meta-frontier Non-radial Directional Distance Function model, this study took energy consumption, the labor force, and fixed asset investments as the inputs, Gross domestic product (GDP) as the desirable output, and CO2 and the Air Quality Index (AQI) scores as the undesirable outputs to assess energy efficiency and air pollutant index efficiency scores in China from 2013–2016 and to identify the areas in which improvements was necessary. It was found that there was a large gap between the western and eastern cities in China. A comparison of the CO2 and AQI in 31 Chinese cities showed a significant difference in the CO2 emissions and AQI efficiency scores, with the lower scoring cities being mainly concentrated in China’s western region. It was therefore concluded that China needs to pay greater attention to the differences in the economic levels, stages of social development, and energy structures in the western cities when developing appropriately focused improvement plans.

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