Association of Air Pollution Levels to Atmospheric Weather Regimes over Europe

The ability to link air pollution to weather regimes may help municipalities to activate in advance plans to protect citizens from severe episodes of air pollution. The aim of this work was to associate the atmospheric circulation patterns and air pollution over Berlin using observational data from three air quality monitoring stations (Urban, Suburban and Traffic stations) during the period January 1990 to December 2002, with weather type’s classifications issued in the frame of the European Cooperation in Science and Technology project (COST733). Results obtained in this work shows similar higher ozone (O3) concentrations and weather regime type during the summer season for both the three stations and the two cluster methods used viz. Self-Organizing Maps (SOM) and cluster analysis of principal components (CAP). The highest concentration level of ozone was observed in the suburban station. High pollution that occurred from the particulate matter (PM10) was observed in the urban station during the winter season. The highest nitrogen dioxide (NO2) value was observed in the traffic station during the spring season. However, the traffic station is probably not a very good indicator of the sensitivity of air quality in meteorology, because it is too much affected by local sources.

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Changes in air-pollution related behaviour measured by Google Trends Search Volume Index in response to reported air quality in Poland

10.21203/rs.3.rs-234681/v1 ◽

2021 ◽

Author(s):

Wojciech Nazar ◽

Katarzyna Plata-Nazar

Keyword(s):

Air Pollution ◽

Air Quality ◽

Winter Season ◽

Google Trends ◽

Volume Index ◽

Quality Data ◽

Coefficient Of Determination ◽

Pollution Levels ◽

Search Volume ◽

Air Quality Data

Abstract Background Decreased air quality is connected to a higher number of hospital admissions and an increase in daily mortality rates. Thus, Poles’ behavioural response to sometimes elevated air pollution levels is vital. The aim of this study was to carry out analysis of changes in air-pollution related information seeking behaviour in response to nationwide reported air quality in Poland. Methods Google Trends Search Volume Index data was used to investigate Poles’ interest in air pollution-related keywords. PM10 and PM2.5 concentrations measured across Poland between 2016 and 2019 were collected from the Chief Inspectorate of Environmental Protection databases. Pearson Product-Moment Correlation and the R2 correlation coefficient of determination were used to measure spatial and seasonal correlations between reported air pollution levels and the popularity of search queries. Results The highest PM10 and PM2.5 concentrations were observed in southern voivodeships and during the winter season. Similar trends were observed for Poles’ interest in air-pollution related keywords. All R2 coefficient of determination values were > 0.5 and all correlations were statistically significant. Conclusion Poland’s air quality does not meet the World Health Organisation guidelines. Also, the air quality is lower in southern Poland and during the winter season. It appears that Poles are aware of this issue and search for daily air quality data in their location. Greater interest in air quality data in Poland strongly correlates with both higher regional and higher seasonal air pollution levels.

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High-resolution PM2.5 forecasting using CAMS predictions, low-cost sensors and ensemble techniques

10.5194/egusphere-egu21-15960 ◽

2021 ◽

Author(s):

Areti Pappa ◽

Ioannis Kioutsioukis

Keyword(s):

Air Pollution ◽

Air Quality ◽

Short Term Memory ◽

Low Cost ◽

Quality Models ◽

Ensemble Techniques ◽

Spatial Coverage ◽

Comparable Performance ◽

Lstm Network ◽

Air Quality Monitoring Stations

Expediting urbanization has triggered an increase in cardiopulmonary diseases attributable to fine-particulate air pollution. Air Quality models simulate the dilution and dispersion of air pollutants that affect the atmosphere, contributing crucially to the comprehension of its processes. Air quality forecasts produced by the Copernicus Atmosphere Monitoring Service (CAMS) provide open access to accurate and reliable information but in a coarse resolution. Data-driven models can downscale the forecasts from deterministic air quality models on the basis of reliable measurements. Low-cost air quality sensors are widely known for their increased spatial coverage and economic operational costs, but usually, their reliability is in dispute. In this study, a dense network of calibrated PM2.5 measurements installed in the city of Patras is combined with CAMS forecasts and statistical approaches to generate 24h forecasts of PM2.5 concentrations in an urban area of Greece. The implemented techniques are the analog ensemble (AnEn) and the Long Short-Term Memory (LSTM) network. Auxiliary variables of meteorological origin were also utilized. The required forecasts were retrieved from the European Center for Medium-Range Weather Forecasts (ECMWF), and were pin-pointed to the location of the air quality monitoring stations. The results showed that both methods had comparable performance, with low bias and relatively small errors. In the stations with high PM2.5 levels, AnEn performed better, whereas in the stations and seasons with moderate concentrations LSTM outperformed. A comprehensive validation is presented and discussed. AnEn and LSTM methods were proved reliable tools for air pollution forecasting and can be used for other regions with small modifications.

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Understanding Public Response to Air Quality Using Tweet Analysis

Social Media + Society ◽

10.1177/2056305119867656 ◽

2019 ◽

Vol 5 (3) ◽

pp. 205630511986765

Author(s):

Supraja Gurajala ◽

Suresh Dhaniyala ◽

Jeanna N. Matthews

Keyword(s):

Air Pollution ◽

Air Quality ◽

Machine Learning Algorithms ◽

Mitigation Measures ◽

Quality Data ◽

Public Health Issue ◽

New Delhi ◽

Public Response ◽

Pollution Levels ◽

Adverse Health Effects

Poor air quality is recognized as a major risk factor for human health globally. Critical to addressing this important public-health issue is the effective dissemination of air quality data, information about adverse health effects, and the necessary mitigation measures. However, recent studies have shown that even when public get data on air quality and understand its importance, people do not necessarily take actions to protect their health or exhibit pro-environmental behaviors to address the problem. Most existing studies on public attitude and response to air quality are based on offline studies, with a limited number of survey participants and over a limited number of geographical locations. For a larger survey size and a wider set of locations, we collected Twitter data for a period of nearly 2 years and analyzed these data for three major cities: Paris, London, and New Delhi. We identify the three hashtags in each city that best correlate the frequency of tweets with local air quality. Using tweets with these hashtags, we determined that people’s response to air quality across all three cities was nearly identical when considering relative changes in air pollution. Using machine-learning algorithms, we determined that health concerns dominated public response when air quality degraded, with the strongest increase in concern being in New Delhi, where pollution levels are the highest among the three cities studied. The public call for political solutions when air quality worsens is consistent with similar findings with offline surveys in other cities. We also conducted an unsupervised learning analysis to extract topics from tweets in Delhi and studied their evolution over time and with changing air quality. Our analysis helped extract relevant words or features associated with different air quality–related topics such as air pollution policy and health. Also, the topic modeling analysis revealed niche topics associated with sporadic air quality events, such as fireworks during festivals and the air quality impact on an outdoor sport event. Our approach shows that a tweet-based analysis can enable social scientists to probe and survey public response to events such as air quality in a timely fashion and help policy makers respond appropriately.

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Air Pollution and Seasonality Effects on Mode Choice in China

Transportation Research Record Journal of the Transportation Research Board ◽

10.3141/2634-15 ◽

2017 ◽

Vol 2634 (1) ◽

pp. 101-109 ◽

Cited By ~ 6

Author(s):

Weibo Li ◽

Maria Kamargianni

Keyword(s):

Air Pollution ◽

Developing Countries ◽

Air Quality ◽

Positive Impact ◽

Mode Choice ◽

Urban Transport ◽

Modal Shift ◽

Pollution Levels ◽

Study Results ◽

The Impact

A modal shift from motorized to nonmotorized vehicles is imperative to reduce air pollution in developing countries. Nevertheless, whether better air quality will improve the willingness to use nonmotorized transport remains unclear. If such a reciprocal effect could be identified, a sort of virtuous circle could be created (i.e., better air quality could result in higher nonmotorized transport demand, which in turn could further reduce air pollution). Developing countries may, therefore, be more incentivized to work on air pollution reduction from other sources to exploit the extra gains in urban transport. This study investigated the impact of air pollution on mode choices and whether nonmotorized transport was preferred when air quality was better. Revealed preference data about the mode choice behavior of the same individuals was collected during two seasons (summer and winter) with different air pollution levels. Two discrete mode choice models were developed (one for each season) to quantify and compare the impacts of different air pollution levels on mode choices. Trip and socioeconomic characteristics also were included in the model to identify changes in their impacts across seasons. Taiyuan, a Chinese city that operates a successful bikesharing scheme, was selected for a case study. The study results showed that air quality improvement had a significant, positive impact on nonmotorized transport use, which suggested that improvements in air quality and promotion of nonmotorized transport must be undertaken simultaneously because of their interdependence. The results of the study could act as a harbinger to policy makers and encourage them to design measures and policies that lead to sustainable travel behavior.

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Brick Sector and Air Quality: An Integrated Assessment towards 2020 Challenge of Environment Development

Environment and Natural Resources Journal ◽

10.32526/ennrj/19/2020203 ◽

2021 ◽

Vol 19 (2) ◽

pp. 153-164

Author(s):

Shazia Pervaiz ◽

◽

Muhammad Ameer Nawaz Akram ◽

Filza Zafar Khan ◽

Kanwal Javid ◽

...

Keyword(s):

Carbon Monoxide ◽

Air Quality ◽

Winter Season ◽

Environmental Parameters ◽

Control Measures ◽

Pollution Levels ◽

Brick Kilns ◽

Central Regions ◽

The Government ◽

Set Up

Brick sector is a mainstay of the urban economy of Punjab. The traditional technology of brick making emits a lot of toxic gases and smoke particulates into air. Hence, the Government of the Punjab, Pakistan announced a ban on low technology brick kiln operations during winter season by the end of December 2020. Initially, the existing set up of brick kilns and air pollution levels were evaluated before and during lockdown period using spatial application. Further, environmental parameters such as aerosols, carbon monoxide, ozone, sulfur dioxide and carbon dioxide were determined to analyze the air quality, including metrological factors. Results of the study exhibited that the upper and central regions of Punjab are the major hubs of brick kilns. So, the level of air quality was inconsistent in the study period due to the existence of large mushrooms of brick kilns. Further, despite lockdown the highest concentration of carbon monoxide was recorded in the eastern side of the province, such as Kasur, Lahore, and Sheikhupura. The level of aerosols also fluctuated and shifted its trends in the central and southern part of the province. While SO2 and CO2 level declined and revealed a satisfactory level of air quality during shutdown. On the other hand, no significant relation to metrological factors, such as rain, is involved in the pollution reduction. Conclusively, the findings of the present study encourage the government agencies to realign the stringent control measures to improve the quality of air in the winter months using the experience of quarantine in 2020.

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Research on Air Quality Analysis Based on Cluster Methods

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/898/1/012024 ◽

2021 ◽

Vol 898 (1) ◽

pp. 012024

Author(s):

Zhaoni Li ◽

Jian Zheng

Keyword(s):

Air Quality ◽

Ambient Air ◽

Quality Analysis ◽

Quality Data ◽

Ambient Air Quality ◽

Republic Of China ◽

Pollution Levels ◽

Analysis Process ◽

Cluster Methods ◽

The Impact

Abstract Research on air quality analysis is a hot field. Here we describe an analysis process based on cluster methods for the data of ambient air quality. In this paper, we use the process to cluster on the air quality data which from the National Urban Air Quality Report in December 2020 on the official website of the Ministry of Ecology and Environment of the People’s Republic of China. We find that cities in different clusters with different main pollutants and pollution levels. Ambient air quality analysis aims to provide guidance for reducing the impact of air pollution on health.

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Research on an Iot Based Air Pollution Monitoring System

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.i1116.0789s219 ◽

2019 ◽

Vol 8 (9S2) ◽

pp. 553-558

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Index ◽

Life Forms ◽

Pollution Monitoring ◽

Wireless Sensor ◽

Pollution Level ◽

Air Pollution Monitoring ◽

Pollution Levels ◽

Different Sources

Humankind, moving to a period centered upon improvement has overlooked the significance of supportability and has been the real guilty party behind the rising Pollution levels in the world's air among all other living life forms. The Pollution levels at certain spots have come to such high degrees that they have begun hurting our very own It will being. An IoT based Air Pollution observing framework incorporates a MQ Series sensor interfaced to a Node MCU outfitted with an ESP8266 WLAN connector to send the sensor perusing to a Thing Speak cloud. Further extent of this work incorporates an appropriate AI model to foresee the air Pollution level and an anticipating model, which is fundamentally a subset of prescient displaying. As age of poisonous gases from ventures, vehicles and different sources is immensely expanding step by step, it winds up hard to control the dangerous gases from dirtying the unadulterated air. In this paper a practical air Pollution observing framework is proposed. This framework can be utilized for observing Pollutions in demeanor of specific territory and to discover the air peculiarity or property examination. The obligated framework will concentrate on the checking of air poisons concentrate with the assistance of mix of Internet of things with wireless sensor systems. The investigation of air quality should be possible by figuring air quality index (AQI)

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Daily variability of air pollution in locations of different population density

E3S Web of Conferences ◽

10.1051/e3sconf/201910000011 ◽

2019 ◽

Vol 100 ◽

pp. 00011

Author(s):

Robert Cichowicz ◽

Artur Stelęgowski

Keyword(s):

Air Pollution ◽

Air Quality ◽

Population Density ◽

Air Pollutants ◽

Daily Maximum ◽

Air Contaminants ◽

Daily Variability ◽

Air Quality Monitoring Stations ◽

Agricultural Areas ◽

Daily Changes

The air quality levels vary during a day, especially in inhabited areas. Therefore, it seems reasonable to observe and analyze the occurrence of daily maximum and minimum level of air pollution. In this article, data obtained from automatic air quality monitoring stations located in 5 large, 5 small and medium cities and 5 villages in Poland was analyzed in 2012−2016. Those locations vary, inter alia, depending on number of inhabitants and population density, and for this reason also due to the presence of air contaminants. As an indicator of daily variability air pollution it was determined the ratio of maximum to minimum concentrations of selected air pollutants (NO2 and NOx, and O3, SO2, CO, PM10 and PM2.5, and benzene) in urban and agricultural areas. In winter, the daily changes were bigger in cities than in villages. While in summer, the level of daily variability was similar, irrespective of size of the settlement unit. The biggest daily changes concerned nitrogen oxides, the lowest − sulfur dioxide and dusts.

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Analysis of a Pollution Transmission Process in Hefei City Based on Mobile Lidar

EPJ Web of Conferences ◽

10.1051/epjconf/202023702006 ◽

2020 ◽

Vol 237 ◽

pp. 02006 ◽

Cited By ~ 1

Author(s):

Shuai Zhang ◽

Zhaoming Zhou ◽

Conglei Ye ◽

Jibing Shi ◽

Peng Wang ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Lidar System ◽

Transmission Process ◽

A New Technique ◽

Air Quality Monitoring Stations ◽

Pollution Transmission ◽

Monitoring Stations

The air pollution has been regional in China with the development of economy. To monitoring the air pollution transmission, a new technique, mobile lidar system (GBQ-S01), was introduced. In this paper, a pollution transmission process happened on October 26th, 2017, was analyzed with the use of mobile lidar, air quality monitoring stations data, and Hysplit backward trajectories. The results showed that the polluted air mass was transferred from northeast under the force of air pressure. Under the influences of air pollution transmission and bad meteorological diffusion conditions, The PM10 quality concentrations in Hefei increased a lot within 5 hours; among all the 10 national air quality monitoring stations, the Luyang District (the northernmost one) and Changjiang Middle Road (the easternmost one) received the most serious impact with PM10 concentration reached up to 252 μg/m3 and 219 μg/m3 at 22:00 (Beijing Time).

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Visualization of uncertainty in air quality ensemble forecasts

10.5194/egusphere-egu21-15612 ◽

2021 ◽

Author(s):

Angelika Heil ◽

Augustin Colette

Keyword(s):

Air Pollution ◽

Time Series ◽

Air Quality ◽

Mobile Apps ◽

Predictive Performance ◽

User Preferences ◽

Ensemble Forecasts ◽

The Public ◽

Pollution Levels ◽

Wide Range

Air quality forecasts help decision-makers to respond to air pollution episodes and to improve air quality management. In recent years, the public increasingly uses mobile apps to check forecasted air pollution levels and then adjusts outdoor activities accordingly. For Europe, state-of-the-art daily air quality forecasts are provided by the regional Copernicus Atmosphere Monitoring System (CAMS). The system integrates forecasts from 9 individual models. This ensemble approach not only achieves better predictive performance compared to a single model, but also allows a better quantification of forecast uncertainty. How to best communicate this uncertainty to a broad audience is by no means a trivial task, but yet essential to maintain trust in the forecasts.We developed innovative visualizations to convey CAMS forecast uncertainties in time series and maps. The development is strongly user-driven and involves iterative consultation with a wide range of expert and non-expert users. We investigate the feasibility of different bivariate techniques to communicate the ensemble's best estimate and its uncertainty in a single map. We explore user preferences for a variety of time-series graphs, including boxplots, violinplots, and fancharts. Whilst preferences are largely driven by the data and visualization literacy of the users, we identify some generally valid best practices in terms of graph types, choices of colors and labels, and accompanying textual explanations. Finally, we present our candidate designs for the public display of air quality forecasts on the regional CAMS webpage.

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