scholarly journals The impact of air quality on its Baidu index: grey model analysis

2021 ◽  
Author(s):  
Leping Tu ◽  
Yan Chen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Grey Correlation ◽  
Convolution Model ◽  
Grey Correlation Analysis ◽  
And Control ◽  
The Impact ◽  
The Relationship

Abstract To investigate the relationship between air quality and its Baidu index, we collect the annual Baidu index of air pollution hazards, causes and responses. Grey correlation analysis, particle swarm optimization and grey multivariate convolution model are used to simulate and forecast the comprehensive air quality index. The result shows that the excessive growth of the comprehensive air quality index will lead to an increase in the corresponding Baidu index. The number of search for the causes of air quality has the closest link with the comprehensive air quality index. Strengthening the awareness of public about air pollution is conducive to the improvement of air quality. The result provides a reference for relevant departments to prevent and control air pollution.

scholarly journals A psychophysical measurement on subjective well-being and air pollution

2019 ◽  
Vol 10 (1) ◽  
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.

scholarly journals Application of Fuzzy Inference System in the Prediction of Air Quality Index

2021 ◽  
Vol 6 (3) ◽  
pp. 75-85
Author(s):  
Nor Hayati Shafii ◽  
Nur Aini Mohd Ramle ◽  
Rohana Alias ◽  
Diana Sirmayunie Md Nasir ◽  
Nur Fatihah Fauzi
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Average Concentration ◽  
Air Quality Index ◽  
Inference System ◽  
Proposed Model ◽  
The Impact

Air pollution is the presence of substances in the atmosphere that are harmful to the health of humans and other living beings. It is caused by solid and liquid particles and certain gases that are suspended in the air.  The air pollution index (API) or also known as air quality index (AQI) is an indicator for the air quality status at any area.  It is commonly used to report the level of severity of air pollution to public and to identify the poor air quality zone.  The AQI value is calculated based on average concentration of air pollutants such as Particulate Matter 10 (PM10), Ozone (O3), Carbon Dioxide (CO2), Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2).  Predicting the value of AQI accurately is crucial to minimize the impact of air pollution on environment and human health.  The work presented here proposes a model to predict the AQI value using fuzzy inference system (FIS). FIS is the most well-known application of fuzzy logic and has been successfully applied in many fields.  This method is proposed as the perfect technique for dealing with environmental well known and tackling the choice made below uncertainty.  There are five levels or indicators of AQI, namely good, moderate, unhealthy, very unhealthy, and hazardous. This measurement is based on classification made from the Department of Environment (DOE) under the Ministry of Science, Technology, and Innovation (MOSTI). The results obtained from the actual data are compared with the results from the proposed model.  With the accuracy rate of 93%, it shows that the proposed model is meeting the highest standard of accuracy in forecasting the AQI value.

COVID-19 Infection and Air Pollution Characteristics in USA

2021 ◽  
Vol 12 (2) ◽  
pp. 16-35
Author(s):  
Sharanpreet Kaur ◽  
Satwinder Singh
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Human Body ◽  
Quality Index ◽  
Human Infection ◽  
Air Quality Index ◽  
Initial Stage ◽  
The Usa ◽  
The Relationship ◽  
Death Cases

Coronavirus is diagnosed as a human-to-human infection at the initial stage by many of the researchers. As coronavirus is primarily targeting the respiratory system of the human body, the study tries to explore the relationship between pollution and increased number of cases in the states of the USA. The objective of the study is to determine whether the air quality index (AQI) of the year 2019 has a concern in the increasing number of coronavirus disease (COVID-19) cases in USA. This study included data of coronavirus confirmed and death cases from the dates January 22nd 2020 to June 30th 2020 for more than 45 states of the USA. Six AQI defining parameters—CO, NO2, Ozone (O3), PM10, PM2.5, and SO2—are selected for the study. The present study tried to identify whether air pollution is playing a significant role in spreading the coronavirus pandemic or not. Results confirmed that air quality index (AQI) defining parameters are not the sole factor behind the rise in the number of coronavirus cases in the USA.

scholarly journals The Relationship Between the Actual Level of Air Pollution and Residents’ Concern about Air Pollution: Evidence from Shanghai, China

2019 ◽  
Vol 16 (23) ◽  
pp. 4784 ◽  
Author(s):  
Daxin Dong ◽  
Xiaowei Xu ◽  
Wen Xu ◽  
Junye Xie
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pollution Level ◽  
Daily Data ◽  
Internet Search Engine ◽  
Beneficial Impact ◽  
Local Residents ◽  
The Relationship

This study explored the relationship between the actual level of air pollution and residents’ concern about air pollution. The actual air pollution level was measured by the air quality index (AQI) reported by environmental monitoring stations, while residents’ concern about air pollution was reflected by the Baidu index using the Internet search engine keywords “Shanghai air quality”. On the basis of the daily data of 2068 days for the city of Shanghai in China over the period between 2 December 2013 and 31 July 2019, a vector autoregression (VAR) model was built for empirical analysis. Estimation results provided three interesting findings. (1) Local residents perceived the deprivation of air quality and expressed their concern on air pollution quickly, within the day on which the air quality index rose. (2) A decline in air quality in another major city, such as Beijing, also raised the concern of Shanghai residents about local air quality. (3) A rise in Shanghai residents’ concern had a beneficial impact on air quality improvement. This study implied that people really cared much about local air quality, and it was beneficial to inform more residents about the situation of local air quality and the risks associated with air pollution.

scholarly journals Analysis of existing air monitoring technologies

2021 ◽  
Vol 3 (134) ◽  
pp. 67-78
Author(s):  
Volodymyr Tarasov ◽  
Bohdan Molodets ◽  
Тatyana Bulanaya ◽  
Oleg Baybuz
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Human Condition ◽  
Air Quality Index ◽  
Air Monitoring ◽  
Atmospheric Air ◽  
Monitoring Technologies ◽  
The Impact

Atmospheric air monitoring is a systematic, long-term assessment of the level of certain types of pollutants by measuring their amount in the open air. Atmospheric air monitoring is an integral part of an effective air quality management system and is carried out through environmental monitoring networks, which should support timely provision of public information about air pollution, support compliance with ambient air quality standards and development of emission strategies, support for air pollution research.The work is devoted to existing air monitoring technologies: ground (sensors, diffusion tubes, etc.) and remote resources (satellites, aircraft, etc.). In addition, standards of air quality assessment (European and American) are described. As an example, we consider the European Air Quality Index (EAQI) and the Air Quality Index according to EPF standards: indicators by which these indices are calculated, the ranking of air status depending on the value of the index are described.AQI (Air Quality Index) is used as an indicator of the impact of air on the human condition. The European Air Quality Index allows users to better understand air quality where they live, work or travel. By displaying information for Europe, users can gain an understanding of air quality in individual countries, regions and cities. The index is based on the values of the concentration of the five main pollutants, including particles less than 10μm (PM10), particles less than 2.5μm (PM2.5), ozone (O3); nitrogen dioxide (NO2); sulfur dioxide (SO2). To conclude, ground stations give a more accurate picture of the state of the air at a point, while satellite image data with a certain error (due to cloud cover, etc.) can cover a larger area and solve the problem of coverage of stations in the area. There is no single standard for calculation. Today, the European Air Quality Index (EAQI) is used in Ukraine and Europe.

scholarly journals Relationship between Visibility, Air Pollution Index and Annual Mortality Rate in Association with the Occurrence of Rainfall—A Probabilistic Approach

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8397
Author(s):  
Grzegorz Majewski ◽  
Bartosz Szeląg ◽  
Anita Białek ◽  
Michał Stachura ◽  
Barbara Wodecka ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Probabilistic Approach ◽  
Pollution Index ◽  
Air Quality Index ◽  
Pollution Indices ◽  
Calculation Algorithm ◽  
Linear Relationships ◽  
The Impact

An innovative method was proposed to facilitate the analyses of meteorological conditions and selected air pollution indices’ influence on visibility, air quality index and mortality. The constructed calculation algorithm is dedicated to simulating the visibility in a single episode, first of all. It was derived after applying logistic regression methodology. It should be stressed that eight visibility thresholds (Vis) were adopted in order to build proper classification models with a number of relevant advantages. At first, there exists the possibility to analyze the impact of independent variables on visibility with the consideration of its’ real variability. Secondly, through the application of the Monte Carlo method and the assumed classification algorithms, it was made possible to model the number of days during a precipitation and no-precipitation periods in a yearly cycle, on which the visibility ranged practically: Vis < 8; Vis = 8–12 km, Vis = 12–16 km, Vis = 16–20 km, Vis = 20–24 km, Vis = 24–28 km, Vis = 28–32 km, Vis > 32 km. The derived algorithm proved a particular role of precipitation and no-precipitation periods in shaping the air visibility phenomena. Higher visibility values and a lower number of days with increased visibility were found for the precipitation period contrary to no-precipitation one. The air quality index was lower for precipitation days, and moreover, strong, non-linear relationships were found between mortality and visibility, considering precipitation and seasonality effects.

scholarly journals Air Pollution Predicts Harsh Moral Judgment

2019 ◽  
Vol 16 (13) ◽  
pp. 2276
Author(s):  
Hongxia Li ◽  
Xue Wang ◽  
Yafei Guo ◽  
Zhansheng Chen ◽  
Fei Teng
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Moral Judgment ◽  
Behavioral Intention ◽  
Behavioral Intentions ◽  
Quality Index ◽  
Past Experience ◽  
Air Quality Index ◽  
Negative Behaviors ◽  
The Relationship

The present research recruited participants from China, which is suffering from serious air pollution, and examined whether air pollution would be associated with moral judgment and immoral behavioral intention. Study 1 (n = 145) used the objective Air Quality Index to indicate the level of air pollution and found that it predicted harsh judgment on others’ moral violations but did not predict judgment on others’ non-moral negative behaviors or their own immoral behavioral intentions. Study 2 (n = 90) asked participants either to recall a past experience of being exposed to air pollution or to recall a neutral experience and consistently found that air pollution only influenced judgment on moral violations. The findings also ruled out the feeling of threat or the trust of government as possible mediators in the relationship between air pollution and harsh moral judgment.

scholarly journals The Effect of Air Pollution on the Occurrence of Nonspecific Conjunctivitis

2016 ◽  
Vol 2016 ◽  
pp. 1-3 ◽  
Author(s):  
Zhiwei Li ◽  
Xiaoyan Bian ◽  
Jianguang Yin ◽  
Xiaoli Zhang ◽  
Guoying Mu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Short Term ◽  
Outpatient Visits ◽  
Lag Effect ◽  
One Year ◽  
Autumn And Winter ◽  
The Relationship

Purpose. To investigate the short-term effect of air pollution on occurrence of nonspecific conjunctivitis. Methods. Data were collected from outpatient visits from cases with conjunctivitis over a period of one year. Regression analysis was performed to evaluate the relationship between the number of outpatient visits and the air quality and the lag effect of air quality on conjunctivitis occurrence. Results. The air quality index on the day of presentation (P=0.023), one day before presentation (P=0.049), and two days before presentation day (P=0.050) had a positive relation with outpatient visits for conjunctivitis. The air quality index (P=0.001) and outpatient visits number per day (P=0.013) in autumn and winter (October to March) were significantly higher than those in spring (April) and summer (September). Conclusions. The air quality index within two days before presentation affected the probability of attending the outpatient clinic for nonspecific conjunctivitis. High number of cases can be expected in colder season.

scholarly journals Implementation of dataflow programming based Fuzzy Logic algorithm for gas concentration index in around of Sidoarjo mudflow, Indonesia

2018 ◽  
Vol 154 ◽  
pp. 03012
Author(s):  
Edita Rosana Widasari ◽  
Barlian Henryranu Prasetio ◽  
Hurriyatul Fitriyah ◽  
Reza Hastuti
Keyword(s):  
Air Pollution ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Quality Index ◽  
Methane Concentration ◽  
Air Quality Index ◽  
Pollution Level ◽  
Affected Area ◽  
Fuzzy Logic Algorithm ◽  
Dataflow Programming

Sidoarjo mudflow or known as Lapindo mudflow erupted since 2006. The Sidoarjo mudflow is located in Sidoarjo City, East Java, Indonesia. The mudflow-affected area has high air pollution level and high health risk. Therefore, in this paper was implemented a system that can categorize the level of air pollution into several categories. The air quality index can be categorized using fuzzy logic algorithm based on the concentration of air pollutant parameters in the mudflow-affected area. Furthermore, Dataflow programming is used to process the fuzzy logic algorithm. Based on the result, the measurement accuracy of the air quality index in the mudflow-affected area has an accuracy rate of 93.92% in Siring Barat, 93.34% in Mindi, and 95.96% in Jatirejo. The methane concentration is passes the standard quality even though the air quality index is safe. Hence, the area is indicated into Hazardous level. In addition, Mindi has highest and stable methane concentration. It means that Mindi has high-risk air pollution.

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