This summary provides the key details from the document in 3 sentences:
The document discusses a research project analyzing the relationship between stock performance and air pollution in Shanghai from 2012-2014. It examines stocks from heavy industry and environmentally-friendly industries, comparing stock returns to air quality measures like PM2.5, PM10, CO2 and SO2. The research found statistically significant relationships between worsening air quality and lower stock returns, especially for heavy industry stocks, though some limitations like omitted variables were noted.
The document analyzes the relationship between stock returns of different industries in Shanghai and air quality. It finds that air quality, particularly PM2.5 levels, has a significant effect on stock returns. Higher PM2.5 is associated with higher returns for heavy industry stocks but lower returns for the overall stock market. Certain pollutants like SO2 and NO2 increased the probability of positive returns for specific heavy industry companies.
I am teaching a unit on linear functions to my 8th grade algebra class over five days. Rather than solely using lectures and textbooks, I will introduce concepts through various online resources, activities, and student-created work to promote excitement in learning. These include social media, videos, math games, robots, and digital tools. Students will be assessed through their independent work solving and graphing linear functions, rather than traditional tests and quizzes. My goal is for all students to accurately understand and apply linear functions by the end of the unit.
Intelligent pollution monitoring using wireless sensor networks eSAT Journals
This document describes an intelligent pollution monitoring system using wireless sensor networks. The system monitors air pollution using gas sensors, water pollution using pH meters, and detects human movement using PIR sensors. When pollution levels exceed standards, it sends SMS alerts to the industry owner and pollution control board. The system aims to control pollution and protect the environment and human health. It provides continuous, real-time pollution monitoring to ensure standards are met.
This document is a research project submitted by David Ng'ang'a to the University of Nairobi investigating indoor air pollution from various cooking stoves. It includes an abstract stating the objectives were to analyze emissions and efficiency of an improved wood stove (Stove A), improved charcoal stove (Stove B), traditional three stone firewood stove, and metallic charcoal stove. Results found Stove A and B reduced levels of carbon monoxide, particulate matter, and carbon dioxide emissions compared to the traditional stoves. Stove B showed the highest efficiency and lowest fuel consumption. The study aims to evaluate stove performance and compare indoor air pollution levels.
Strategy of control of urban air pollutionECRD2015
Vehicular emissions are the largest contributor to urban air pollution, accounting for 70% of emissions. A long-term strategy is needed to achieve clean air. The proposed strategy includes 6 steps: 1) Reduce need for car travel through better urban planning and public transport. 2) Reduce emissions from vehicles in use by promoting cleaner fuels and technologies. 3) Reduce emissions from household and commercial activities like refuse burning. 4) Improve industry performance through new technologies and compliance. 5) Fund research to improve understanding of air quality issues. 6) Improve planning to minimize exposure to pollution and encourage cleaner production.
Air pollution monitoring system using mobile gprs sensors arraySaurabh Giratkar
This paper contain brief introduction to vehicular pollution, effect of increase in vehicular pollution on environment as well on human health. To monitor this pollution wireless sensor network (WSN) system is proposed. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution- Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies.
Descriptive analysis of awareness about land pollution, water pollution, air ...Poonam Sankhe
The document provides an introduction to various types of pollution - air, water, land and noise pollution. It discusses their causes and effects. It begins with defining pollution and providing a brief history of pollution dating back to the Stone Age. It then defines each type of pollution in 1-2 sentences and lists some of their key disadvantages. For air pollution, it notes how it affects health and climate change. For water pollution, it mentions excess fertilizers and animal waste as common causes. Land pollution is discussed in the context of agricultural chemicals and industrial wastes affecting soil quality. Noise pollution is defined as disturbing or excessive noise impacting life.
C3 1 Research Methods And Writing Research ProposalsMahmoud
This chapter introduces scientific research and its key characteristics. Scientific research is defined as a systematic, controlled, empirical, and critical investigation of hypothetical propositions about relationships between observed phenomena. All research begins with a basic question about a specific phenomenon. The chapter contrasts the scientific method with other approaches such as the method of tenacity, intuition, and authority. The scientific method establishes truth through a series of objective analyses rather than relying on a single source or what has always been believed.
The document analyzes the relationship between stock returns of different industries in Shanghai and air quality. It finds that air quality, particularly PM2.5 levels, has a significant effect on stock returns. Higher PM2.5 is associated with higher returns for heavy industry stocks but lower returns for the overall stock market. Certain pollutants like SO2 and NO2 increased the probability of positive returns for specific heavy industry companies.
I am teaching a unit on linear functions to my 8th grade algebra class over five days. Rather than solely using lectures and textbooks, I will introduce concepts through various online resources, activities, and student-created work to promote excitement in learning. These include social media, videos, math games, robots, and digital tools. Students will be assessed through their independent work solving and graphing linear functions, rather than traditional tests and quizzes. My goal is for all students to accurately understand and apply linear functions by the end of the unit.
Intelligent pollution monitoring using wireless sensor networks eSAT Journals
This document describes an intelligent pollution monitoring system using wireless sensor networks. The system monitors air pollution using gas sensors, water pollution using pH meters, and detects human movement using PIR sensors. When pollution levels exceed standards, it sends SMS alerts to the industry owner and pollution control board. The system aims to control pollution and protect the environment and human health. It provides continuous, real-time pollution monitoring to ensure standards are met.
This document is a research project submitted by David Ng'ang'a to the University of Nairobi investigating indoor air pollution from various cooking stoves. It includes an abstract stating the objectives were to analyze emissions and efficiency of an improved wood stove (Stove A), improved charcoal stove (Stove B), traditional three stone firewood stove, and metallic charcoal stove. Results found Stove A and B reduced levels of carbon monoxide, particulate matter, and carbon dioxide emissions compared to the traditional stoves. Stove B showed the highest efficiency and lowest fuel consumption. The study aims to evaluate stove performance and compare indoor air pollution levels.
Strategy of control of urban air pollutionECRD2015
Vehicular emissions are the largest contributor to urban air pollution, accounting for 70% of emissions. A long-term strategy is needed to achieve clean air. The proposed strategy includes 6 steps: 1) Reduce need for car travel through better urban planning and public transport. 2) Reduce emissions from vehicles in use by promoting cleaner fuels and technologies. 3) Reduce emissions from household and commercial activities like refuse burning. 4) Improve industry performance through new technologies and compliance. 5) Fund research to improve understanding of air quality issues. 6) Improve planning to minimize exposure to pollution and encourage cleaner production.
Air pollution monitoring system using mobile gprs sensors arraySaurabh Giratkar
This paper contain brief introduction to vehicular pollution, effect of increase in vehicular pollution on environment as well on human health. To monitor this pollution wireless sensor network (WSN) system is proposed. The proposed system consists of a Mobile Data-Acquisition Unit (Mobile-DAQ) and a fixed Internet-Enabled Pollution Monitoring Server (Pollution-Server). The Mobile-DAQ unit integrates a single-chip microcontroller, air pollution sensors array, a General Packet Radio Service Modem (GPRS-Modem), and a Global Positioning System Module (GPS-Module). The Pollution-Server is a high-end personal computer application server with Internet connectivity. The Mobile-DAQ unit gathers air pollutants levels (CO, NO2, and SO2), and packs them in a frame with the GPS physical location, time, and date. The frame is subsequently uploaded to the GPRS-Modem and transmitted to the Pollution-Server via the public mobile network. A database server is attached to the Pollution- Server for storing the pollutants level for further usage by various clients such as environment protection agencies, vehicles registration authorities, and tourist and insurance companies.
Descriptive analysis of awareness about land pollution, water pollution, air ...Poonam Sankhe
The document provides an introduction to various types of pollution - air, water, land and noise pollution. It discusses their causes and effects. It begins with defining pollution and providing a brief history of pollution dating back to the Stone Age. It then defines each type of pollution in 1-2 sentences and lists some of their key disadvantages. For air pollution, it notes how it affects health and climate change. For water pollution, it mentions excess fertilizers and animal waste as common causes. Land pollution is discussed in the context of agricultural chemicals and industrial wastes affecting soil quality. Noise pollution is defined as disturbing or excessive noise impacting life.
C3 1 Research Methods And Writing Research ProposalsMahmoud
This chapter introduces scientific research and its key characteristics. Scientific research is defined as a systematic, controlled, empirical, and critical investigation of hypothetical propositions about relationships between observed phenomena. All research begins with a basic question about a specific phenomenon. The chapter contrasts the scientific method with other approaches such as the method of tenacity, intuition, and authority. The scientific method establishes truth through a series of objective analyses rather than relying on a single source or what has always been believed.
- Air pollution causes over 13,000 premature deaths per year in South Korea. Fine particulate matter (PM2.5) is the main culprit, responsible for over 12,000 deaths from lung cancer, heart disease, and stroke.
- The South Korean government has invested billions to reduce emissions from diesel vehicles and install filters, but air quality has worsened since 2013. Meteorological conditions are also playing a role, with weaker winds leading to increased air stagnation.
- South Korea has launched a National Strategic Research Project with four goals: evaluating pollution sources, improving forecasting, strengthening emissions controls, and reducing population exposure. The project aims to inform policymaking through interdisciplinary collaboration between scientists, industry, and
IRJET- Analysis and Prediction of Air QualityIRJET Journal
This document discusses air pollution prediction using machine learning techniques. It begins with an introduction to air pollution, defining it as the introduction of harmful substances into the atmosphere. The six main criteria air pollutants are then described: ozone, particulate matter, carbon monoxide, nitrogen dioxide, sulfur dioxide, and lead. Common machine learning techniques for air pollution prediction are also introduced, including supervised learning algorithms like random forests and support vector machines, and unsupervised learning algorithms like k-means clustering. The document concludes that machine learning provides opportunities for improved air pollution prediction by analyzing historical pollution data.
1.needs to be done in 8 hours.2.focus on how haze comes.3.3 kendahudson
This research article analyzes trends in PM2.5 concentrations across five major Chinese cities from 1999-2008. It finds that PM2.5 levels decreased the most during warmer seasons and were highest in the evening and winter. Even with reductions, PM2.5 concentrations still exceed Chinese standards, especially in northern regions during winter. Stronger policies are needed to reduce energy use and optimize air quality, particularly in northern China in winter.
1. The study analyzed the impact of COVID-19 lockdown measures on air quality in major Indian cities using machine learning techniques. Meteorological normalization was used to remove the effects of weather factors from pollutant concentration data.
2. Various deep learning models were used to forecast pollutant concentrations and compare observed concentrations during lockdown. This identified significant reductions in most pollutants during lockdown compared to business-as-usual forecasts.
3. A novel hybrid deep learning-cuckoo search approach was proposed and developed to more accurately forecast pollutant concentrations by optimizing deep learning hyperparameters. This approach can help policymakers develop long-term air quality strategies.
TitleResearch article Trends of PM2.5 concentrations in China A l.docxrowthechang
This research article analyzes trends in PM2.5 concentrations in five major Chinese cities between 1999-2008. The study finds that PM2.5 levels decreased overall during this period, especially in warmer seasons, but remained highest during evening hours and winter months. Additionally, PM2.5 concentrations were still above Chinese national air quality standards, particularly in northern regions during winter. Stronger government measures are needed to reduce emissions, especially in northern China during colder periods.
This document discusses air pollution indices and how they are used. Air pollution indices transform weighted air pollution parameter values like SPM, SO2, CO, NO2, O3, and hydrocarbons into a single number to simply and clearly indicate the air quality level. They inform the public about daily pollution changes, help compare cities, and evaluate enforcement policies. Common calculation methods include relating parameters to standards, averaging ratios to standards, and assigning sub-index values within parameter ranges. Air pollution indices provide a useful way to track air quality changes and facilitate comparisons.
This document summarizes an exploratory data analysis (EDA) of air pollution data from major Chinese cities conducted using IBM's Watson Analytics. The analysis found that northern Chinese cities generally had higher levels of air pollution than southern cities, as indicated by higher emissions, PM10 concentrations, and fewer days of good air quality. Population levels and industrial activity contributed more to air pollution than other factors like living emissions. The EDA process involved initial questions from Watson Analytics, developing new questions, experimenting with filters and visualizations, and refining the data.
Assessment of Variation in Concentration of Air Pollutants Within Monitoring ...IRJET Journal
This document summarizes a study that assessed variation in air pollutant concentration across 12 monitoring stations in Mumbai, India. The study analyzed monthly pollution data from 2020-2021 for 7 pollutants (PM2.5, PM10, NO2, NH3, SO2, CO, ozone) from the Central Pollution Control Board. Statistical analysis using ANOVA and Tukey's HSD post-hoc test identified pairs of stations without significant differences in pollutant concentrations across all 7 pollutants. This analysis could help identify redundant monitoring stations. The study also used logistic regression to predict air quality class based on factors like tree cover, population density, temperature, and more.
Air Pollution Prediction via Differential Evolution Strategies with Random Fo...IRJET Journal
This document discusses using a hybrid machine learning technique combining differential evolution and random forest methods to predict air pollution levels. It analyzes data on various pollutants from two cities in India - Delhi and Patna. The proposed approach is experimentally validated to achieve better performance compared to independent classifiers and multi-label classifiers in terms of accuracy, area under the curve, success index and correlation. Differential evolution is used to initialize population and optimize candidate solutions. Random forest creates an ensemble of decision trees to make predictions. The hybrid method is tested on predicting carbon monoxide, nitrogen dioxide and benzene levels using data from a monitoring station in Delhi.
STUDY ON AMBIENT AIR QUALITY MONITORING FROM GOVT POLYTECHNIC COLLEGE TO GUTT...IRJET Journal
This document summarizes a study on ambient air quality monitoring between Government Polytechnic College and Guttur Road in Harihara City, India. Air quality was monitored over two months at five sites for pollutants SPM, SO2, and NO2. In April and May, air quality was best at A K Colony and Guttur Road with an impact of minimal. Government Polytechnic College and Shivamogga Circle had satisfactory air quality with minor impacts. Harapanahalli Circle had the highest pollution levels and moderate impacts including breathing difficulties. Overall, the study found air quality within permissible limits according to the air quality index.
IRJET - Air Quality Index – A Study to Assess the Air QualityIRJET Journal
This document discusses a study on assessing air quality in Delhi, India using the Air Quality Index (AQI). It provides background on air pollution and the importance of measuring AQI. The study calculates daily AQI values over three years for Delhi based on concentrations of pollutants like NO2, SO2, SPM and RSPM. The results show AQI values were regularly unhealthy around 200. SPM and RSPM correlated most strongly with AQI, suggesting they are major contributors to air pollution. Stricter measures are needed to address rising levels of particulate matter and improve air quality.
IRJET - Prediction of Air Pollutant Concentration using Deep LearningIRJET Journal
This document describes a study that used artificial neural networks to predict PM2.5 air pollutant concentration levels in Manali, Chennai, India. The study collected data on PM2.5 levels as well as factors like NO2, SO2, CO, relative humidity, and wind speed over a three year period. An artificial neural network model with feed-forward backpropagation was developed using this data, with 6 input nodes (for each pollutant/factor) and 1 output node (for PM2.5 levels). The model was trained on 70% of the data and tested on the remaining 15%, achieving a correlation coefficient between predicted and observed PM2.5 of 0.8. The study demonstrated that artificial neural
This study analyzed air pollution data from 2015-2020 in Hyderabad, India to investigate temporal variations and correlations between pollutants. Principal component analysis identified two components explaining 65.5% of variation; PM2.5 and PM10 were the key parameters influencing air quality. Results showed annual oscillations and seasonal patterns in pollutants, with frequently exceeding standards. Pollutant levels were highest in winter and lowest in monsoon season. Transportation, industry and construction were determined to be contributing to rising air pollution levels in Hyderabad over the study period.
1. The study assessed Beijing's air quality improvement in response to intensified control strategies between 2013-2019 by analyzing pollution monitoring data and influencing factors.
2. The results showed measurable reductions in most pollutants and increases in good air quality days, with the most significant decreases occurring for SO2, followed by CO, PM2.5, PM10, and NO2.
3. The control of coal consumption played a dominant role in reducing pollutants, while meteorological conditions and emissions controls also contributed to the observed air quality improvements over the study period.
Analysis and Prediction of Air Quality in IndiaIRJET Journal
This document discusses analyzing and predicting air quality in India using machine learning algorithms. The authors collected air quality data from various Indian cities from 2018 to 2021, including levels of pollutants like PM2.5, PM10, NO2, O3, and SO2. They analyzed trends over time and the impact of lockdowns. Different time series forecasting algorithms (ARIMA, Prophet, LSTM, ETS) were used to predict future air quality levels. ARIMA provided the best predictions. The analyses and predictions were developed into a dashboard application to visualize trends and comparisons of the different algorithms. The work provides a model for predicting air quality that can help identify heavily polluted regions and reverse air pollution in India
Long-Term Weather Behaviour - Asia FocusMitch Leung
A quantitative and qualitative study on the impacts of long-term weather behaviour and its impact on air quality and climate change. The study takes into account of data from India and China.
A Deep Learning Based Air Quality PredictionDereck Downing
The document discusses using deep learning techniques to predict air quality. Specifically, it proposes using a Long Short-Term Memory (LSTM) model to predict hourly air quality index values. The LSTM model is trained on historical air quality and meteorological data. The proposed LSTM model is found to outperform existing models at predicting air quality, as measured by a lower root mean square error (RMSE) value for predictions. The document aims to develop techniques for accurately forecasting air quality to help address increasing air pollution issues.
An environmental impact assessment was conducted for a proposed integrated steel plant in Odisha, India. The summary finds:
1) Ambient air quality monitoring found existing PM10 and PM2.5 levels above national standards in the project area. Dispersion modeling also predicted the plant would significantly increase air pollution.
2) The EIA report underestimated health impacts by missing secondary particulate formation and incremental PM2.5 impacts. It also did not account for mercury or heavy metal emissions.
3) Based on estimated annual emissions of 9433 tons of PM, 13,131 tons of NOx, and 11,642 tons of SO2, a health impact assessment was conducted and found significant impacts from increased
This report analyzes PM2.5 air quality data from over 3000 cities around the world in 2018. It finds that Asian cities dominate the rankings for highest PM2.5 levels, with Delhi, India and Dhaka, Bangladesh having the highest annual averages. At a country level, Bangladesh had the highest average PM2.5 levels when weighted by population. The report also examines air quality by region and finds that regions like the Middle East and South Asia had very high percentages of cities exceeding WHO air quality guidelines. It concludes that while more areas are monitoring air quality, large parts of the world still lack access to real-time public air quality data.
Generated a Statistical Report on air quality of Ireland (correlation and regression) using SPSS and religious belief of different age group people in their respective religion(Two way ANOVA) using R.
- Air pollution causes over 13,000 premature deaths per year in South Korea. Fine particulate matter (PM2.5) is the main culprit, responsible for over 12,000 deaths from lung cancer, heart disease, and stroke.
- The South Korean government has invested billions to reduce emissions from diesel vehicles and install filters, but air quality has worsened since 2013. Meteorological conditions are also playing a role, with weaker winds leading to increased air stagnation.
- South Korea has launched a National Strategic Research Project with four goals: evaluating pollution sources, improving forecasting, strengthening emissions controls, and reducing population exposure. The project aims to inform policymaking through interdisciplinary collaboration between scientists, industry, and
IRJET- Analysis and Prediction of Air QualityIRJET Journal
This document discusses air pollution prediction using machine learning techniques. It begins with an introduction to air pollution, defining it as the introduction of harmful substances into the atmosphere. The six main criteria air pollutants are then described: ozone, particulate matter, carbon monoxide, nitrogen dioxide, sulfur dioxide, and lead. Common machine learning techniques for air pollution prediction are also introduced, including supervised learning algorithms like random forests and support vector machines, and unsupervised learning algorithms like k-means clustering. The document concludes that machine learning provides opportunities for improved air pollution prediction by analyzing historical pollution data.
1.needs to be done in 8 hours.2.focus on how haze comes.3.3 kendahudson
This research article analyzes trends in PM2.5 concentrations across five major Chinese cities from 1999-2008. It finds that PM2.5 levels decreased the most during warmer seasons and were highest in the evening and winter. Even with reductions, PM2.5 concentrations still exceed Chinese standards, especially in northern regions during winter. Stronger policies are needed to reduce energy use and optimize air quality, particularly in northern China in winter.
1. The study analyzed the impact of COVID-19 lockdown measures on air quality in major Indian cities using machine learning techniques. Meteorological normalization was used to remove the effects of weather factors from pollutant concentration data.
2. Various deep learning models were used to forecast pollutant concentrations and compare observed concentrations during lockdown. This identified significant reductions in most pollutants during lockdown compared to business-as-usual forecasts.
3. A novel hybrid deep learning-cuckoo search approach was proposed and developed to more accurately forecast pollutant concentrations by optimizing deep learning hyperparameters. This approach can help policymakers develop long-term air quality strategies.
TitleResearch article Trends of PM2.5 concentrations in China A l.docxrowthechang
This research article analyzes trends in PM2.5 concentrations in five major Chinese cities between 1999-2008. The study finds that PM2.5 levels decreased overall during this period, especially in warmer seasons, but remained highest during evening hours and winter months. Additionally, PM2.5 concentrations were still above Chinese national air quality standards, particularly in northern regions during winter. Stronger government measures are needed to reduce emissions, especially in northern China during colder periods.
This document discusses air pollution indices and how they are used. Air pollution indices transform weighted air pollution parameter values like SPM, SO2, CO, NO2, O3, and hydrocarbons into a single number to simply and clearly indicate the air quality level. They inform the public about daily pollution changes, help compare cities, and evaluate enforcement policies. Common calculation methods include relating parameters to standards, averaging ratios to standards, and assigning sub-index values within parameter ranges. Air pollution indices provide a useful way to track air quality changes and facilitate comparisons.
This document summarizes an exploratory data analysis (EDA) of air pollution data from major Chinese cities conducted using IBM's Watson Analytics. The analysis found that northern Chinese cities generally had higher levels of air pollution than southern cities, as indicated by higher emissions, PM10 concentrations, and fewer days of good air quality. Population levels and industrial activity contributed more to air pollution than other factors like living emissions. The EDA process involved initial questions from Watson Analytics, developing new questions, experimenting with filters and visualizations, and refining the data.
Assessment of Variation in Concentration of Air Pollutants Within Monitoring ...IRJET Journal
This document summarizes a study that assessed variation in air pollutant concentration across 12 monitoring stations in Mumbai, India. The study analyzed monthly pollution data from 2020-2021 for 7 pollutants (PM2.5, PM10, NO2, NH3, SO2, CO, ozone) from the Central Pollution Control Board. Statistical analysis using ANOVA and Tukey's HSD post-hoc test identified pairs of stations without significant differences in pollutant concentrations across all 7 pollutants. This analysis could help identify redundant monitoring stations. The study also used logistic regression to predict air quality class based on factors like tree cover, population density, temperature, and more.
Air Pollution Prediction via Differential Evolution Strategies with Random Fo...IRJET Journal
This document discusses using a hybrid machine learning technique combining differential evolution and random forest methods to predict air pollution levels. It analyzes data on various pollutants from two cities in India - Delhi and Patna. The proposed approach is experimentally validated to achieve better performance compared to independent classifiers and multi-label classifiers in terms of accuracy, area under the curve, success index and correlation. Differential evolution is used to initialize population and optimize candidate solutions. Random forest creates an ensemble of decision trees to make predictions. The hybrid method is tested on predicting carbon monoxide, nitrogen dioxide and benzene levels using data from a monitoring station in Delhi.
STUDY ON AMBIENT AIR QUALITY MONITORING FROM GOVT POLYTECHNIC COLLEGE TO GUTT...IRJET Journal
This document summarizes a study on ambient air quality monitoring between Government Polytechnic College and Guttur Road in Harihara City, India. Air quality was monitored over two months at five sites for pollutants SPM, SO2, and NO2. In April and May, air quality was best at A K Colony and Guttur Road with an impact of minimal. Government Polytechnic College and Shivamogga Circle had satisfactory air quality with minor impacts. Harapanahalli Circle had the highest pollution levels and moderate impacts including breathing difficulties. Overall, the study found air quality within permissible limits according to the air quality index.
IRJET - Air Quality Index – A Study to Assess the Air QualityIRJET Journal
This document discusses a study on assessing air quality in Delhi, India using the Air Quality Index (AQI). It provides background on air pollution and the importance of measuring AQI. The study calculates daily AQI values over three years for Delhi based on concentrations of pollutants like NO2, SO2, SPM and RSPM. The results show AQI values were regularly unhealthy around 200. SPM and RSPM correlated most strongly with AQI, suggesting they are major contributors to air pollution. Stricter measures are needed to address rising levels of particulate matter and improve air quality.
IRJET - Prediction of Air Pollutant Concentration using Deep LearningIRJET Journal
This document describes a study that used artificial neural networks to predict PM2.5 air pollutant concentration levels in Manali, Chennai, India. The study collected data on PM2.5 levels as well as factors like NO2, SO2, CO, relative humidity, and wind speed over a three year period. An artificial neural network model with feed-forward backpropagation was developed using this data, with 6 input nodes (for each pollutant/factor) and 1 output node (for PM2.5 levels). The model was trained on 70% of the data and tested on the remaining 15%, achieving a correlation coefficient between predicted and observed PM2.5 of 0.8. The study demonstrated that artificial neural
This study analyzed air pollution data from 2015-2020 in Hyderabad, India to investigate temporal variations and correlations between pollutants. Principal component analysis identified two components explaining 65.5% of variation; PM2.5 and PM10 were the key parameters influencing air quality. Results showed annual oscillations and seasonal patterns in pollutants, with frequently exceeding standards. Pollutant levels were highest in winter and lowest in monsoon season. Transportation, industry and construction were determined to be contributing to rising air pollution levels in Hyderabad over the study period.
1. The study assessed Beijing's air quality improvement in response to intensified control strategies between 2013-2019 by analyzing pollution monitoring data and influencing factors.
2. The results showed measurable reductions in most pollutants and increases in good air quality days, with the most significant decreases occurring for SO2, followed by CO, PM2.5, PM10, and NO2.
3. The control of coal consumption played a dominant role in reducing pollutants, while meteorological conditions and emissions controls also contributed to the observed air quality improvements over the study period.
Analysis and Prediction of Air Quality in IndiaIRJET Journal
This document discusses analyzing and predicting air quality in India using machine learning algorithms. The authors collected air quality data from various Indian cities from 2018 to 2021, including levels of pollutants like PM2.5, PM10, NO2, O3, and SO2. They analyzed trends over time and the impact of lockdowns. Different time series forecasting algorithms (ARIMA, Prophet, LSTM, ETS) were used to predict future air quality levels. ARIMA provided the best predictions. The analyses and predictions were developed into a dashboard application to visualize trends and comparisons of the different algorithms. The work provides a model for predicting air quality that can help identify heavily polluted regions and reverse air pollution in India
Long-Term Weather Behaviour - Asia FocusMitch Leung
A quantitative and qualitative study on the impacts of long-term weather behaviour and its impact on air quality and climate change. The study takes into account of data from India and China.
A Deep Learning Based Air Quality PredictionDereck Downing
The document discusses using deep learning techniques to predict air quality. Specifically, it proposes using a Long Short-Term Memory (LSTM) model to predict hourly air quality index values. The LSTM model is trained on historical air quality and meteorological data. The proposed LSTM model is found to outperform existing models at predicting air quality, as measured by a lower root mean square error (RMSE) value for predictions. The document aims to develop techniques for accurately forecasting air quality to help address increasing air pollution issues.
An environmental impact assessment was conducted for a proposed integrated steel plant in Odisha, India. The summary finds:
1) Ambient air quality monitoring found existing PM10 and PM2.5 levels above national standards in the project area. Dispersion modeling also predicted the plant would significantly increase air pollution.
2) The EIA report underestimated health impacts by missing secondary particulate formation and incremental PM2.5 impacts. It also did not account for mercury or heavy metal emissions.
3) Based on estimated annual emissions of 9433 tons of PM, 13,131 tons of NOx, and 11,642 tons of SO2, a health impact assessment was conducted and found significant impacts from increased
This report analyzes PM2.5 air quality data from over 3000 cities around the world in 2018. It finds that Asian cities dominate the rankings for highest PM2.5 levels, with Delhi, India and Dhaka, Bangladesh having the highest annual averages. At a country level, Bangladesh had the highest average PM2.5 levels when weighted by population. The report also examines air quality by region and finds that regions like the Middle East and South Asia had very high percentages of cities exceeding WHO air quality guidelines. It concludes that while more areas are monitoring air quality, large parts of the world still lack access to real-time public air quality data.
Generated a Statistical Report on air quality of Ireland (correlation and regression) using SPSS and religious belief of different age group people in their respective religion(Two way ANOVA) using R.
Similar to Stock Performance and Air Pollution (20)
1. FINC 625 Team 7 Project
Stock Performance and Air Pollution (haze,PM2.5) in
Shanghai
By
Dingteng Huang
Liang Shuang
Nan Li
Puyi Fang
Yiwei Yan
2. Abstract
There is a lot of research shows that air quality has significant influence on a nation’s economic
growth. Our research paper examines the relationship between stock return and air quality. Our main
target stocks are those of Heavy Industry and environmental friendly industry in Shanghai. Our main
air quality parameters are PM 2.5, PM10, CO2, And SO2. The time period we choose is from 2012
to 2014. After conducting numerous regressions analysis of different stock returns. We conclude
that air quality has significant influence on stock returns statistically and economically. However, we
also find some flaws with our regression models, one of which is the omitted variables. There are
also some observations that suggest the relationship between air quality and stock returns is not
simply linear. However, that is beyond the scope of our research paper.
Introduction & Background
In recent years, the big economic growth of Asia has attracted the rest of world’s attention.
There are increasingly a lot of investment opportunities because of the rapid growth of
economy. Especially, China plays an essential role in Asian economical development. From 1979
until 2010, China's average annual GDP growth was 9.91%, reaching an historical high of 15.2% in
1984 and a record low of 3.8% in 1990. China's nominal GDP by Expenditure approach surpassed
that of Italy in 2000, France in 2002, the United Kingdom in 2006 and that of Germany in 2007,
before overtaking Japan in 2009, making China the world's second largest economy after the United
3. States. In the annual year of 2014, China’s GDP growth rate is 7.4%, which is lower than before but
still a very attractive figure. Looking at the first quarter of 2015, the GDP growth rate of china is
about 7%.
Undoubtedly, China has plenty of opportunities to equity investors. Shanghai stock exchange
(SSE) is becoming a new rising star in the world’s largest stock indexes. Besides, Hongkong stock
exchange and Tokyo Stock exchange still play the major role in Asia area, and in November 14,
2014, China lunched “stock connection” between Shanghai and Hongkong stock exchange, relaxed
the restrictions that historically split the Chinese stock market between shares targeted at local
investors and those available to international investors. Another big issue that comes to people’s
attention is the air quality of China in recent years. Beijing and Shanghai suffer very terrible air
pollution in recent years. One of the air quality figures “PM 2.5” has become a very popular stuff in
people’s real life. Other air quality figures are PM10, SO2, NO2, and so on. The air quality certainly
has influence on people’s life, work, and health. In history, there has been a lot of research analysis
of among stock index, oil price, foreign exchange price, government policy and so on. However,
few people took analysis of the relationship between stock return and air quality. Our group is very
curious about whether the air quality has a significant relationship with stock returns. Based on this
idea, we conducted the regression analysis of the Shanghai stock index and Shanghai air quality
during the period of 2012- 2014.
Hypothesis
● Air quality, especially PM 2.5 has a significant effect on various stock returns.
● The more terrible the air quality is, the higher the stock return in heavy industry.
Data Collection Process
The first big part of our analysis is to find the relevant data and integrate it to the single excel
file. This is a very time consuming process. We gathered varies Shanghai stock price data during the
period of 2012-2014 from Chinese official economic website. We then collected the air quality data
of the same period. After having these data available, we begin conduct our research process. Before
4. getting there, we want to introduce the dependent and independent variables. We try to give you a
big picture of the subjects we are working for. A sample data sheet is provided in Exhibit1.
Dependent Variables:
● AdjClose is adjusted daily price of ShangHai Stock Exchange (SSE)
● SSEDiscete-n is the discrete stock return of (SSE)
● SSELogReturn is the log return of SSE
● SPCReturn is Shanghai petrochemical company stock return
● SHEReturn is Shanghai Electric Power stock return
● SPEReturn is Shanghai DaTun Energy Company stock return
● MotorReturn is Shanghai Motor company stock return
● BaoReturn is shanghai Bao Steel Company stock return
● PortReturn is Portfolio Return
● PorPorsiti-n is a binary variable that equal 1 if portfolio return is positive, otherwise is
negative.
● SEEPostive-n is a binary variable that equal 1 if SEE stock return is positive, otherwise is
negative.
● LKReturn and SHRreturn are two newly environmental company stock returns
● PM25PorRe is portfolio return, which includes two environmental company’s stock
Independent Variables:
● PM25Average is the Average daily figure of PM 2.5
● PM25Max is the highest PM 2.5 reached each day
● PM10 is the Average daily figure of PM 10
● SO2 is the Average daily figure of SO2
● NO2 is the Average daily figure of NO2
Seasonality
Q2, Q3, Q4 represents the second quarter, third quarter, and fourth quarter respectively. They
are dummy variables, whose base group is Q1, the first quarter. For example, if the data we collected
is in the first quarter of a year, then Q2=Q3=Q4=0. If data is belong to the second quarter, Q2=1, and
Q3=Q4=0. The seasonality has a significant impact on both stock price and air quality. For instance,
the air quality is much more terrible in the winter (the fourth quarter) and there is a so called
“January Effect” on stock price.
● Unheal-25100 represents if PM 2.5 is greater than 100, it is unhealthy. It is a dummy
variable. For example, if PM2.5 is greater than 100, then Unheal-25100 is equal one,
otherwise it is equal zero.
● ReturnSpread is the difference between the portfolio return and SSE stock return.
Correlation and Co-linearity
5. Because our independent variables that are related to air pollution is highly correlated to PM2.5
index, co-linearity problem is the first task we have to solve in order to get better regression results.
Correlation matrix of those variables is showing in Exhibit2. To solve the problem, we ran a
variance inflation factor test on all variables and targeting the variable that have a VIF higher than
hour. We than regress those variables against our main variable (PM2.5Average) and use the
residuals to replace the original value of targeted variables. The new variables are defined as follows:
● ResPMmaxOn is residuals of the regression process between PM max and PM 2.5Average
● ResPM10Onis residuals of the regression process between PM10 and PM2.5 Average
● ResSO2OnPM is residuals of the regression process between SO2 and PM2.5 Average
● ResNO2onPM is residuals of the regression process between NO2 and PM2.5 Average
Research Logic
To begin our research on the relationship between daily stock return and air quality in
Shanghai, we first need to make every possible regression from all the observations we have.
Different type of regression and variables modifications were used to enhance the coverage of our
research. Well-tuned filtering mechanisms were developed to compare and test regressions. Finally,
based on the “winning” regression results left in our pool, we were able to maintain meaningful and
significant conclusions. For better demonstration, our logic and process waterfall are showing as
follow:
6. Regression Analysis and Findings
Our analysis and findings are primarily built upon the regressions that passed all filters and
are in our final selection pool. Regressions in the end selection pool are divided into three major
categories include “OLS uses same-day Ys and Xs”, “OLS uses lagged Ys”, and “Probit Binary
Regression uses Lagged binary Ys”. In the following report, each category is denoted as “Tier I”,
“Tier II”, and “Tier III” accordingly. All regression result from the final pool is listed in Exhibit(R).
Tier I Same Day OLS
● SSE Composite Index Return (000001.SS)
SSEDiscreteReturn = 0.0007745 – 0.00136*PM25Average + 0.002541*Q4 + u
(0.0007228) (0.00111) (0.0009748)
For regression test result, please see Exhibit3. The regression have an F-stat(2,722) of 2.94
which is significant at 95% confidence level. Daily average PM2.5 index and dummy variable for
quarter 1 of each year are jointly significant. A negative beta of PM2.5 index, that is significant at
80%, aligned with our hypothesis that, within the same day, a 1 mg/m3increases in PM2.5 will
negatively impact the return of SSE Composite Index daily return by 0.136%. This effect is
depressed for the fourth quarter of each year due to the seasonality of PM2.5 index. This regression
will only pass Ramsey RESET test at 75% confidence level, which indicates omitted variable biases.
However, we expect this problem to be within most of our regression because our independent
variables only focus on air pollution and seasonality while its impact on Index return is only partial.
Therefore, we believe this type of problem does not conflict with our research topic very much. The
same logic applies to later analysis.
● Shanghai Motor Company (600688.SS)
7. MotorReturn = 0.002379 – 0.006*PM25Average – 0.00516*PM10 + 0.0031055*Q4 +
(0.0023685) (-0.00304) (-0.00467) (0.0017958)
0.0032212*UnhealthyPM25100 + μ
(0.002299)
For regression test result, please see Exhibit4. Although the F-test of this regression is only
significant at 80% confidence level, the t-stat of the coefficient on PM2.5 daily average reached -
1.97, which is statistical significant at 95%. This relationship is fairly that we can clearly tell from
Exhibit5. We find this interesting because people usually relate PM2.5 and any sort of air pollution
to automotive exhaust. And this negative relationship between auto maker’s stock return support that
common view. The other possible explanation of the relationship could be that when air quality,
especially PM2.5 is too high, or the index exceeds consumer tend to avoid outdoor activity. Thus,
fewer cars are sold during the day which indicates less return. In addition, stated on the previous
article, Good Day Sunshine: Stock Return and Weather (2001) David Hirshlefer and Tyler Shumway,
psychological evidence and casual intuition predict that sunny good weather is associated with
upbeat mood. Thus we believe this negative relationship between Shanghai Motor Company stock
return and daily PM2.5 index is economical significant.
● Shanghai Bao Steel Company
BaoReturn = 0.0002857 – 0.00153*PM2.5Averag + 0.001205*Q3 + 0.0037935*Q4 + μ
(0.0011943) (0.00153) (0.00137) (0.00133)
For regression test result, please see Exhibit6. The regression has an F-stat (3,721) of 2.91
which is significant at 95% confidence level. All regressors are jointly significant. Steel industry has
always been a large source of pollutions historically. And the view is widely shared by the general
public, especially after the pollution in China became a serious topic. The fact that PM2.5 index has
an negative impact on the return of Bao Steel, at some level showing the market’s reaction towards
air pollution in Shanghai.
Tier II Lag 1 Day OLS
8. ● SSE Composite Index Return (000001.SS)
SSEDiscreteReturn = -0.0016134 + 0.00507*S02 + 0. 00197*N02
(0.0011369) (0.00409) (0.00201)
+ 0.0013873*Q3 + 0.001821*Q4 – 0.0022324*UnhealthyPM2.5+ μ
(0.001049) (0.001087) (0.0011373)
For regression test result please see Exhibit7. The regression have an F-stat(5,710) of 2.10 which
is significant at 94% confidence level. Daily average SO2 index, average N02, dummy variable for
quarter 3 and 4 of each year, and dummy variable for unhealthy PM 2.5 index of that day are jointly
significant. A negative beta of unhealthy PM 2.5 index, that is significant at 95%, aligned with our
hypothesis that, if the former day’s PM 2.5 index exceed the healthy level, for the following day the
return of SSE Composite Index daily return will decrease by 0.22324%. This effect is amplified for
the third and fourth quarter of each year.
● Shanghai Petrochemical Company
SPCReturn = -0.0021294 + 0. 00975*PM10 + 0. 0083*NO2
(0.0021294) (0.00578) (0.00449)
– 0.0045505*UnhealthyPM2.5 + μ
(0.0026083)
For regression test result, please see Exhibit8. The regression have an F-stat(3,720) of 2.14
which is significant at 91% confidence level. Daily average PM 10 average, average NO2 and
dummy variable for unhealthy PM 2.5 index of that day are jointly significant. A negative beta of
unhealthy PM 2.5 index, that is significant at 92% confidence level, aligned with out hypothesis. If
the previous day’s PM2.5 index exceeds the healthy level, for the following day, the return of
Shanghai Petrochemical Company will decrease by 0.45505%. A positive beta of PM 10 index and
daily average NO2, that is significant at 90% confidence level. For the following day, a 1 mg/m3
increases in PM 10 will positively impact the return of SPC daily return by 0. 975%, a 1 mg/m3
increases in NO2 will positively impact the return of SPC daily return by 0. 83%. We assume that
9. this result is due to the fact that, SPC is the largest petrochemical company located in Shanghai,
which engaged in production of ethylene, fiber, resin and plastics. One of main pollution sources of
NO2 and PM 10 in Shanghai are come from SPC. Therefore, the increase of the pollutant indicates
that SPC’s production or sale increased for that day. As a result the company’s share goes up.
● Shanghai Electric Power Company
SHEReturn = -0.0045377 – 0.0037*PM2.5Max + 0.00299*PM10 + 0.01808*SO2
(0.0021054) (0.00447) (0.00433) (0.00709)
+0.00239*NO2 + 0.0034606*Q3 – 0.0060768*UnhealthyPM2.5 + μ
(0.00363) (0.0018325) (0.002122)
For regression test result, please see Exhibit9. The regression has an F-stat (6,717) of 2.31 which
is significant at 95% confidence level. Daily average PM 2.5 Max, PM 10, SO2, NO2 and dummy
variable for quarter 3 and unhealthy PM 2.5 index of that day are jointly significant. A positive beta
of SO2, that is significant at 99% confidence level (T-stat = 2.55), also a negative beta of Unhealthy
PM 2.5 index (T-stat = -2.88), that is significant at 99% confidence level, aligned with out
hypothesis. Scatter Plot shows the strong relationship in Exhibit10. If the former day’s PM 2.5
indexes exceed the healthy level, for the following day the return of Shanghai Electric Power
Company will decrease by 0.60768%. A positive beta of daily average NO2, that is significant at
99% confidence level. For the following day, a 1 mg/m3
increases in NO2 will positively impact the
return of SHE daily return by 1.808%. We assume that this result is due to SHE is the largest electric
power company located in Shanghai. The coal burning plants are the biggest pollution source of
PM2.5 and NO2. The biggest coal consuming factory at Shanghai is the SHE Company. Therefore,
the increase of the pollutant NO2 indicates that SHE’s production or sale increased for that day. As a
result the company’s share goes up.
● Shanghai Bao steel Company
10. BaoReturn = -0.0021287 – 0.000642*PM2.5 + 0.00519*NO2 + 0.0016734*Q3
(0.0014452) (0.00234) (0.00283) (0.0013792)
+0.0031895*Q4 – 0.0020612*UnhealthyPM2.5 + μ
(0.0016541) (0.0016541)
For regression test, result please see Exhibit11. The regression has an F-stat (5,718) of 2.38 which is
significant at 95% confidence level. Daily average PM 2.5, NO2 and dummy variable for quarter
3&4 and unhealthy PM 2.5 index of that day are jointly significant. The coefficient result is similar
to the SPC since they are both industrial based. And the similar results from same day OLS further
proves that the return of BaoSteel is strongly related to air quality in Shanghai.
● Industry Portfolio
PortReturn = -0.0005641 + 0.00657*SO2 + 0.0012949*Q4 –
(0.001037) (0.00524) (0.0014055)
0.0025843*UnhealthyPM2.5 + μ
(0.0014632)
For regression test result please see Exhibit12. The regression has an F-stat (3,720) of 1.62 which is
significant at 80% confidence level.
● PM2.5 Environmental Friendly Portfolio
PM2.5PortReturn = 0.0031607 + 0.00462*PM2.5 – 0.01482*SO2 – 0.0022936*Q2 + μ
(0.0021151) (0.00395) (0.001094) (0.0024741)
For regression test result please see Exhibit13. The regression has an F-stat (3,720) of 0.75
which is only significant at 50% confidence level. Although the F-stat of this model is low, the result
is interesting. This is the only model that the coefficient of SO2 is negative and the intercept is a
positive number. We think that the portfolio is consisted of environmental friendly stocks, if the SO2
increased by a 1 mg/m3
, the following day return of portfolio will decrease by 1.482%. The
environmental friendly industry is the emerging market in China nowadays, at meanwhile the
11. industrial firms are under strike. The high growth rate of Chinese GDP is contributed by the
industrial firms which heavily depend on the coal. However, this high growth brings China critical
pollution problems. Therefore how to balance the pollution of heavy industry and sustainable
development is the biggest economic issue in China now. Therefore, Chinese government economic
policy are becoming more and more favorable to the environmental friendly firms, and the heavy
industry firms are restricted by production limitation and forced to spend more on pollutant disposal.
As a result of this, the intercept proves that the returns of these environmental friendly firms tend to
be positive all the time while the returns of the heavy industry firms are not.
Tier 3 Binary Dependent Variable Probit Regression
In this part, we use the Logit regression to analyze relationship between the binary dependent
variables and the probability of several positive target returns.
Shanghai Electric Power (SHE)
Pr( SHEPositiveReturn =1| X1, X2, … , Xk ) = G(-0.2997554 -0.0055828*PM25MAX +
(0.1707965) (0.48503)
0.00149137*SO2 – 0.2963043*Q3 – 0.3226641*UnhealthyPM25100 + μ)
(0.72272)(0.1821412) (0.2013937)
For regression test results please see Exhibit14. The regression has a chi (4) of 7.64 with 90%
confidence level. Daily maximum PM2.5 index, average SO2, dummy variable for quarter 3 of each
year, and dummy variable for unhealthy PM 2.5 index of that day are joint significant. A positive
beta of average SO2 index with 96% confidence level, on the next trading day, a 1mg/m3 increase in
SO2 will positively impact the probability of positive return of Shanghai Electric Power (SHE) by
G(1.49%). This effect decreased on quarter 3 of each year by 2.96% with 90% confidence level.
Also, a negative beta of unhealthy PM 2.5 index with 89% confidence level, for the following
trading day the SHE will decrease by 0.3226641%.We conclude that the reason behind that is the
same as we discussed above in Tier 2.
12. ● SSEComposite Index Return (000001.SS)
Pr(SSEDiscreteReturn =1|X1, X2, … , Xk) = G(-0.1380309 +0.005589*PM25MAX
(0.2257337) (0.47463)
+ 0.0052701*NO2 - 0.2691055*Q2 + 0.2678245*Q3 – 0.1472397*UnhealthyPM25100 + μ)
(0.37009)(0.19131) (0.20083) (0.18815)
For regression test, result please see Exhibit15. The regression has a chi2 ( 5) of 9.49 with91%
confidence level. Daily maximum PM2.5 index, average NO2, dummy variables for quarter 2 and 3
of each year and dummy variable for unhealthy PM2.5 index of that day are jointly significant. A
positive beta of daily average NO2 index with 85% confidence level, a 1 mg/m3 increase in NO2 will
positively impact the probability of positive daily return of SSE Composite Index by G (0.52701%)
on the next trading day. We conclude that the higher discharging of NO2 usually will increasing the
probability of positive return of SSE index, the reason behind that is heavy industries is one of the
most economic lifeline in China, besides NO2 is the worst polluter in China, which means high level
of NO2 discharging usually stems from the rapid development of heavy industries.
● Environmental Friendly Portfolio
Pr(PM25PortPositiveReturn =1| X1, X2, … , Xk) = G(0.0655521 +0.0097548*PM10+
(0.0884624) (0.4317)
0.2549341*Q3 +μ)
(0.1777918)
For regression test result, please see Exhibit16. The regression has a chi2 (2) of 5.70 with 91%
confidence level. Daily average PM 10 index and dummy variable for quarter 3 of each year are
joint significant. A positive beta of daily average PM 10 index with 98% confidence level, a 1
mg/m3 increase in daily average PM 10 will positively impact the possibility of positive daily
return of our environmental friendly portfolio G(0.97548%) on the next trading day. This
situation amplified on quarter 3 of each year. The reason behind that is higher PM 10 index
13. usually arouse the focus on developing environment friendly industries from the society, which
would push forward the development of environment friendly industries. For the scatter plot,
please see Exhibit17.
Pr(PortPositiveReturn =1| X1, X2, … , Xk) = G(0.0782299 + 0.0058157*PM25MAX
(0.111321) (0.45879)
+ 0.21751*Q3 – 0.1988713*UnhealthyPM25100 + μ)
(0.180038) (0.1684623)
For regression test result, please see Exhibit18. The regression has a chi2( 3) of 4.70 with 81%
confidence level. Daily maximum PM 2.5 index, dummy variables for quarter 3 of each year and
unhealthy PM 2.5 index of that day are joint. A positive beta of daily maximum PM 2.5 index
with 80% confidence level, a 1 mg/m3 increase in daily maximum PM 2.5 will positively impact
the possibility of the positive daily return of our heavy industries portfolio G(0.58157%) on the
next trading day. The reason behind that is the same as we discussed above in Tier 2.
We concluded that all pollution indexes (such as daily average SO2, average NO2, average PM
10, maximum PM 2.5) have a positive relation with all the probability of positive target returns
(such as Shanghai Electric Power, SSE Composite Index, Environmental Friendly and Heavy
Industries portfolio) respectively.
Conclusion
We also find that “3rd and 4th quarter effect” of each year has appeared in most of our
meaningful regressions. Therefore we can conclude that, maybe due to the specific climate in
Shanghai, air quality in fall and winter is much worse than the rest of a year.
14. Bibliography
1. The U.S. Department of State, Reporting of Daily Air Quality-Air Quality Index (AQI )(2012
to 2014), http://www.epa.gov/ttn/oarpg/t1/memoranda/rg701.pdf.,
2. David Hirshleifer and Tyler Shumway†, Good Day Sunshine: Stock Returns and the
Weather∗ ,2001
3. Mitra Akhtari, Reassessment of the Weather Effect: Stock Prices and Wall Street
Weather,2011
4.