Analysis of Statistical Trends of Future Air Pollutants for Accurate Prediction

The climate change may be mitigated, and intra air quality assessment and local human well-being can benefit from a decrease in emission of pollutant content in the air. Monitoring the quality of the air around us is one way to do this. However, a location with various emission sources and short-term fluctuations in emissions in both time and space, and changes in winds, temperature, and precipitation creates a complex and variable pollution concentration field in the atmosphere. Therefore, based on the time and location where the sample is obtained, the measurement conducted are reflected in the monitoring results. This study aims to investigate one of India's most polluted cities' air quality measurements by greenhouse gas emissions. Using the Mann-Kendall and Sen's slope estimators, the research piece gives a statistical trend analysis of several air contaminants based on previous pollution data from Mumbai, India's air quality index station. In addition, future levels of air pollution may be correctly forecasted using an autoregressive integrated moving average model. This is followed by comparing different air quality standards and forecasts for future air pollution levels.

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AI powered IoT based Real-Time Air Pollution Monitoring and Forecasting

Journal of Physics Conference Series ◽

10.1088/1742-6596/2115/1/012016 ◽

2021 ◽

Vol 2115 (1) ◽

pp. 012016

Author(s):

Geetha Mani ◽

Joshi Kumar Viswanadhapalli ◽

P Sriramalakshmi

Keyword(s):

Air Pollution ◽

Air Pollutants ◽

Moving Average ◽

Pollution Monitoring ◽

Model Parameters ◽

Air Pollution Monitoring ◽

Moving Average Model ◽

Monitoring And Forecasting ◽

Life On Earth ◽

Auto Regression

Abstract Air is one of the most fundamental constituents for the sustenance of life on earth. The consumption of non-renewable energy sources and industrial parameters steadily increases air pollution. These factors affect the welfare and prosperity of life on earth; therefore, the nature of Air Quality in our environment needs to be monitored continuously. This paper presents the execution and plan of Internet-of-Things (IoT) based Air Pollution Monitoring and Forecasting utilising Artificial Intelligent (AI) methods. Also, Online Dashboard was created for real-time monitoring of Air pollutants (both live and forecasted data) through ‘firebase’ from the Google cloud server. The air pollutants like Carbon Mono Oxide (CO), Ammonia (NH3), and Ozone (O3) layer information are collected from IoT-based sensor nodes in Vijayawada Region. Time Series modelling techniques like the Naive Bayes Model, Auto Regression Model (AR), Auto Regression Moving Average Model (ARMA), and Auto-Regression Integrating Moving Average Model (ARIMA) used to forecast the individual air pollutants aforementioned. The data collected from the IoT sensor node with a time frame is fed as input features for training the model, and optimised model parameters are obtained. The obtained model parameters are again verified with new unseen data for time. The performances of various Time Series models are validated with the help of performance indices like Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The machine learning algorithm flashed in Raspberry Pi-3. It acts as an edge computing device. The current air pollutants data and forecasted data are monitored for the next 4 hours through an online dashboard created in an open-source firebase from Google cloud service.

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Case Study on Anthropogenic Air Pollution in Nagpur City

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.34082 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 2279-2282

Author(s):

Mukul Dayaramani

Keyword(s):

Air Pollution ◽

Air Quality ◽

Harmful Effect ◽

Central India ◽

Well Being ◽

Good Health ◽

Air Pollutant ◽

Corporate Community ◽

Health And Well Being

Air pollution is a very serious problem worldwide. Anthropogenic air pollution is mostly related to the combustion of various types of fuels. Air pollutant levels remain too high and air quality problems are still not solved. The presence of pollutants in the air has a harmful effect on the human health and the environment. Good air quality is a prerequisite for our good health and well-being. Nagpur city is located in Maharashtra state of central India. Business hub and increased industrialization in study area is affecting the environment adversely. n. Changing life style of corporate community and their effects on other population enhancing the contamination of environment

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Visualization of Correlation for Air Pollution Time Series using TAQMN Data

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6620.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 7922-7927

Keyword(s):

Air Pollution ◽

Time Series ◽

Particulate Matter ◽

Pearson Correlation ◽

Fine Particulate Matter ◽

Correlation Method ◽

Well Being ◽

Pollution Levels ◽

Fine Particulate ◽

Pm2.5 Concentration

In Taiwan country Annan, Chiayi, Giran, and Puzi cities are facing a serious fine particulate matter (PM2.5) issue. To date the impressive advance has been made toward understanding the PM2.5 issue, counting special temporal characterization, driving variables and well-being impacted. However, notable research as has been done on the interaction of the content between the selected cities of Taiwan country for particulate matter (PM2.5) concentration. In this paper, we purposed a visualization technique based on this principle of the visualization, cross-correlation method and also the time-series concentration with particulate matter (PM2.5) for different cities in Taiwan. The visualization also shows that the correlation between the different meteorological factors as well as the different air pollution pollutants for particular cities in Taiwan. This visualization approach helps to determine the concentration of the air pollution levels in different cities and also determine the Pearson correlation, r values of selected cities are Annan, Puzi, Giran, and Wugu.

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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 Pollutants Prediction in Shenzhen Based on ARIMA and Prophet Method

E3S Web of Conferences ◽

10.1051/e3sconf/201913605001 ◽

2019 ◽

Vol 136 ◽

pp. 05001 ◽

Cited By ~ 2

Author(s):

Ziyuan Ye

Keyword(s):

Air Quality ◽

Hybrid Model ◽

Air Pollutants ◽

Moving Average ◽

Mixing Time ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Autoregressive Integrated Moving Average ◽

Moving Average Model ◽

Air Quality Monitoring Stations

In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.

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A psychophysical measurement on subjective well-being and air pollution

Nature Communications ◽

10.1038/s41467-019-13459-w ◽

2019 ◽

Vol 10 (1) ◽

Cited By ~ 4

Author(s):

Yuan Li ◽

Dabo Guan ◽

Yanni Yu ◽

Stephen Westland ◽

Daoping Wang ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Quality Index ◽

Well Being ◽

Air Quality Index ◽

Subjective Well Being ◽

Atmospheric Conditions ◽

Survey Techniques ◽

Physical Effects ◽

The Impact

AbstractAlthough the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people’s subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent’s SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI.

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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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Indoor and Outdoor Air Pollution Monitoring Developments in the Municipality of Thessaloniki – Preliminary Actions from Three European Funded Projects

Bulletin of the Geological Society of Greece ◽

10.12681/bgsg.20611 ◽

2019 ◽

Vol 55 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Georgios Papastergios ◽

Paraskevi Tzoumaka ◽

Apostolos Kelessis

Keyword(s):

Air Pollution ◽

Air Quality ◽

Well Being ◽

Pollution Monitoring ◽

Successful Implementation ◽

Outdoor Air ◽

Air Pollution Monitoring ◽

Outdoor Air Pollution ◽

The City ◽

Indoor And Outdoor

Air pollution has been one of the first environmental problems to be addressed by the EU and for this reason clean air is considered essential to good health. Information availability and understanding of the air quality issue is essential part of tackling it with efficiency. Having the latter in mind, the Municipality of Thessaloniki has considered relative environmental actions as an important priority and made significant efforts to include them in its short-term and long-term, already developed, strategies. Through these strategies the Municipality became partner in three important EU funded projects that are dealing with indoor and outdoor air pollution monitoring actions, namely CUTLER, AIRTHINGS, and LIFE SMART IN'AIR. The successful implementation of these projects will add to the knowledge of indoor and outdoor air quality in the City of Thessaloniki, whereas, at the same time, will improve the resilience of the city and the well being of its citizens.

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CHARACTERIZATION OF VOLATILE ORGANIC COMPOUNDS AND ODOUR POLLUTION IN ATMOSPHERE AT OIL AND GASOLINE HANDLING AND PROCESSING IMPACT ZONE

Environment Technology Resources Proceedings of the International Scientific and Practical Conference ◽

10.17770/etr2019vol1.4144 ◽

2019 ◽

Vol 1 ◽

pp. 171

Author(s):

Evita Muizniece-Treija ◽

Iveta Šteinberga

Keyword(s):

Air Quality ◽

Human Health ◽

Well Being ◽

Pollution Level ◽

Gaseous Pollutants ◽

Monitoring Site ◽

Short Term ◽

Limit Values ◽

Private Monitoring ◽

Pollution Levels

Air quality pollution problem is still one of the crucial points for citizens in Europe for already receiving increasing attention, particularly because of the major European cities 10 and more years. Although the EU's long-term goal is to achieve levels of air quality that do not impact and risks to human health and the environment, many of member states still didn`t reach stated goals. Additionally, to gaseous pollutants, recently specific type of pollution, - odour, seems to become more important. Usually in order to determine pollution levels, national, municipal and private monitoring equipment is used. For this research municipal monitoring site in Riga (Latvia), at Milgravja Street 10, controlling gaseous pollutants (SO2, O3, BTX, PM10) and airborne particulate matter, and private monitoring results from Riga, Milgravja Street 16, where odour pollution was obtained, are analysed. Distance between both stations are just 500 m. Measurements at municipal monitoring site is obtained by DOAS and gravimetric sampling, while at Milgravja 16 by photoionization method or so-called “electronic nose”. Monitoring results in municipal station show that in 2017 the average benzene concentration was 4,87 ug/m3, toluene – 8,89 ug /m3 and xylene – 5,07 ug/m3, while the odour pollution level does not exceed 5 odour units. In general estimation of pollution averaged annually do not show and explain variability of pollution levels. It`s well known that high BTX and odour pollution episodes occur in shorter periods, thus short term limit values would be useful in order to characterize short term effects on human health and well-being.

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Air Pollutants Reduce The Physical Activity Of Professional Soccer Players.

10.21203/rs.3.rs-558780/v1 ◽

2021 ◽

Author(s):

Michał Zacharko ◽

Robert Cichowicz ◽

Marcin Andrzejewski ◽

Paweł Chmura ◽

Edward Kowalczuk ◽

...

Keyword(s):

Physical Activity ◽

Air Pollution ◽

Air Quality ◽

Air Pollutants ◽

Well Being ◽

Soccer Players ◽

Professional Soccer ◽

Considerable Problem ◽

The Impact ◽

Sporting Performance

Abstract The aim of the study was to determine the impact of air quality – analyzed on the basis of the model of integrating three types of air pollutants (ozone – O3, particulate matter - PM, nitrogen dioxide – NO2) – on the physical activity of soccer players. Study material consisted of 8927 individual match observations of 461 players competing in the German Bundesliga during the 2017/2018 and 2018/2019 domestic seasons. The measured indices included players’ physical activities: total distance (TD) and high intensity effort (HIE). Statistical analysis showed that with increasing levels of air pollution, both TD (F = 13.900(3); p = 0.001) and HIE (F = 8.060(3); p = 0.001) decrease significantly. The worsening of just one parameter of air pollution results in a significant reduction in performance. This is important information as air pollution is currently a considerable problem for many countries. Improving air quality during training sessions and sports competitions will result in better well-being and sporting performance of athletes, and will also help protect athletes from negative health effects caused by air pollution.

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