A comparative assessment of air pollutants of smog in wagah border and other sites in Lahore, Pakistan

Smog has become the fifth season of Pakistan especially in Lahore city. Increased level of air pollutants (primary and secondary) are thought to be responsible for the formation of smog in Lahore. Therefore, the current study was carried out for the evaluation of air pollutants (primary and secondary) of smog in Wagah border particularly and other sites (Jail road, Gulburg) Lahore. For this purpose, baseline data on winter smog from March to December on primary and secondary air pollutants and meteorological parameters was collected from Environmental Protection Department and Pakistan Meteorological Department respectively. Devices being used in both departments for analysis of parameters were also studied. Collected data was further statistically analyzed to determine the correlation of parameters with meteorological conditions and was subjected to air quality index. According to results, PM 10 and PM 2.5 were found very high above the NEQS. NOx concentrations were also high above the permissible limits whereas SO2 and O3 were found below the NEQS thus have no roles in smog formation. Air Quality Index (AQI) of pollutants was PM 2.5(86-227), PM 10 (46-332), NOx (26-110), O3 (19-84) and SO2 (10-95). AQI of PM 2.5 remained between moderate to very unhealthy levels. AQI of PM 10 remained between good to hazardous levels. AQI of NOx remained between good to unhealthy for sensitive groups’ levels. AQI of O3 and SO2 remained between good to moderate levels. Pearson correlation showed that every pollutant has a different relation with different or same parameters in different areas. It is concluded from the present study that particulate matter was much more responsible for smog formation. Although NOx also played role in smog formation. So there is need to reduce sources of particulate matter and NOx specifically in order to reduce smog formation in Lahore.

A New Collaborative Multi-Agent Monte Carlo Simulation Model for Spatial Correlation of Air Pollution Global Risk Assessment

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index-based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time-series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series-based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short-term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi-agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real-world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.

Forecasting of outdoor air quality index using adaptive Neuro Fuzzy Inference System

Introduction: The estimation of air pollution level is well indicated by Air Quality Index (AQI), which tells how unhealthy the ambient air is and how polluted it can become in near future. Hence, the predictions or modeling of AQI is always of greater concern among researchers and this present study aims to develop such a model for forecasting the AQI. Materials and methods: A combination of Artificial Neural Network (ANN) and Fuzzy logic (FL) system, called Adaptive Neuro-Fuzzy Inference System (ANFIS) have been considered for model development. Daily air quality data (PM2.5 and PM10) and meteorological data (temperature and humidity) over a period of March 2020 to March 2021 were used as the input data and AQI as the output variable for the ANFIS model. The performances of models were evaluated based on Root Mean Square Error (RMSE), Regression coefficient (R2) and Average Absolute Relative Deviation (AARD). Results: A total of 100 datasets is split into training (70), testing (15) and simulation (15). Gaussian and Constant membership functions were employed for classifications and the final index consisted of 81 inference (IF/THEN) rules. The ANFIS Simulation result shows an R2 and RMSE value of 0.9872 and 0.0287 respectively. Conclusion: According to the results from this study, ANFIS based AQI is a comprehensive tool for classification of air quality and it is inclined to produce accurate results. Therefore, local authorities in air quality assessment and management schemes can apply these reliable and suitable results.

ENVIRONMENTAL POLLUTION: EFFECT ON PEDESTRIANS WHILE WALKING IN URBAN STREET

Purpose: The reason for this study is because of observed difference in environmental condition in Lagos metropolis. The change is witnessed in environmental change arising from air, water and noise pollution mostly from increasing vehicle emissions in the State. This study has been conducted to analyze the environmental effects of pollution on pedestrians. Specific objectives are determine the air quality of the city at most populate headquarters of each of the 20 Local Government Areas of Lagos State, to examine the impact of pollution (air, water and noise) on pedestrians and assess various measures for reducing environmental pollution in the State. Methodology: The use of Thermo scientific MIE pDR-1500 instrument was used to measure air quality index of the selected locations and survey was carried out with well-structured questionnaire to elicit information with the aid of incidental sampling technique on impact of pollution on pedestrians from 177 respondents. Findings: Air Quality Index was shown with histogram chart where six out of 20 Local Government Areas are above the acceptable standard of pollution. There is rising cases of pollution in the State and very few Local governments were within acceptable range. One –Sample T-test showed that air pollution is majorly affecting pedestrians with t-value of 22.226 followed by noise with 19.643 and water with 5.529 respectively. Conclusion and recommendations: The research concluded that, there is need to control the rising cases of pollution in the state and policies to tame air and noise pollution in the state should be adopted. Emission control strategies to be adopted with the existing ones can be in form of restricting hours of movement of vehicles to late at night to avoid human pollutant contact, encourage tree planting and rapid evacuation of environmental waste.

A Review of Chronological Evolution of Air Quality Indexing Systems (1966 To 2021)

Air quality index (AQI) also known as air pollution index (API) is the way of describing ambient air quality to assess the health risk associated with pollution. With the advent of time, there have been several air quality indexing systems starting from the first air Quality Index developed in 1966 by Marvin H. Green and various modifications have been made ever since to improve the accuracy of measurement. Such systems can assess the air quality by several factors like the concentration of different pollutants or by various empirically established formulas based on past experiences. In this review article, an effort has been made to chronologically evaluate the AQI system developed across the world from 1966 to 2021. Every indexing system has its own unique method for air quality determination and each method has its own merits and demerits. This pape rcovers various parameters, empirical relationships, standards, merits, and demerits, which in hind sight will help to develop an amalgamation of various indexing systems that can be used as a standard method for monitoring the quality of air. This paper also covers the AQI systems that prevail in India. A fuzzy logic system is very helpful in handling the uncertainty in air quality assessment. So, fuzzy-based air quality indexing systems developed from 2010 to 2017 have also been reviewed. The review of articles established that the results obtained through fuzzybased AQI aremore reliable than the other methods. Out of all the above describing methods, fuzzy synthetic evaluation-based AQI system and fuzzy air quality health index (FAQHI) are more powerful tools to describe the air quality. But till 2017, thereis no development of AQI systems based on fuzzy logic, considering PM2.5 as one of the pollutants. So, there is a need to develop the fuzzy-based AQI system considering PM2.5 as a pollutant with other air pollutants.

Physics experiments using arduino: determination of the air quality index

Air pollution is one of our day’s significant reasons for human health problems and affects every community throughout the world. Monitoring air pollution is a key aspect of raising awareness and pollution mitigation approaches followed by different nations. This paper targets to develop a low-cost Internet of things-based embedded system to measure and maintain air quality index (AQI) indicators at any locality. The system implemented here is minimal and can be deployed quickly and easily. The AQI measurement system was developed and tested for several periods, and recorded values of AQI were found to be in close agreement with actual values obtained from standard databases. In addition, several starting physics and electronics laboratory courses train students on measuring physical parameters over time. In this context, along with the introduction to the current pollution scenario and the challenges, this experiment will give a first-hand exposure of setting up a simple experiment and measuring a physical parameter to time. Students also learn to write simple programs and interface the experiment with a computer to record the results. The current work also demonstrates how to publish/subscribe the data using the message queue telemetry transport protocol.