Air We Share: Unveiling Two Decades of Rising Aerosol Threats in Hyderabad
Innovative Study Uncovers Alarming Aerosol Surge in Hyderabad: A twenty-year analysis by NIT, Tiruchirappalli’s Civil Engineering scholars reveals a startling 45% increase in aerosol pollution, challenging both summer and winter air quality norms. Unveiling critical insights into the escalating environmental crisis, this research marks a pivotal moment for public health and policy reform in India’s burgeoning urban landscapes.
“In a landmark study titled ‘Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India,’ researchers Aneesh Mathew, Padala Rajasekhar, and Nandan AK from the Civil Engineering Department of the National Institute of Technology (NIT), Tiruchirappalli, have exposed a critical environmental concern.
Over a meticulous 20-year investigation (2002–2021), leveraging MODIS satellite data, the team discovered a concerning 45% hike in aerosol concentration levels in Hyderabad’s atmosphere. This revelation not only challenges the existing understanding of seasonal pollution patterns but also signals an urgent call to action for mitigating air quality degradation in one of India’s fastest-growing urban centers. The study’s findings, correlating aerosol levels with adverse climatic and health impacts, underscore the pressing need for comprehensive environmental strategies and public health policies tailored to the realities of climate change and urban expansion.
Findings
The examination of daily mean data unveiled a concerning rising trend in the number of days with severe AOD (>1), indicating an escalation in air pollution levels over time. Furthermore, the study delved into the seasonal and monthly mean AOD data from 2017 to 2022, highlighting noteworthy fluctuations in peak AOD values, particularly during the summer, autumn, and winter seasons.
Trend Analysis Techniques
Various trend analysis techniques were employed, including the Mann–Kendall, modified Mann–Kendall, and innovative trend analysis (ITA) tests. Collectively, these tests revealed a significant and consistent increase in AOD from 2002 through 2021, with statistically significant results (p < 0.05; Z > 0), reinforcing the gravity of the escalating trend in aerosol concentrations over the years.
Implications & Recommendations
The observed escalating trend in aerosol concentrations underscores the pressing need for immediate intervention strategies to combat air pollution in the Hyderabad district. The study’s findings provide a critical foundation for policymakers and public health officials to develop targeted policies and solutions to mitigate the adverse effects of air pollution on public health and the environment.
Significance of the Study
This study’s comprehensive analysis of aerosol concentrations over a substantial timeframe contributes significantly to the body of knowledge surrounding air pollution in the Hyderabad district. By elucidating the long-term trends in aerosol levels and their implications, the study aids in informed decision-making processes and the development of proactive measures to address the detrimental effects of air pollution.
Here is the Summary:
Concerns of Air Pollution:
– Air pollution is a significant concern, impacting human health and the economy.
– Particulate matter (PM) and aerosols are identified as key contributors to adverse impacts.
Study Objectives:
– The study aimed to analyze aerosol concentrations over 20 years in Hyderabad, India.
– Data from MODIS sensors was used to assess daily, monthly and seasonal trends.
Trend Analysis:
– A rising trend in the number of days with severe AOD (>1) was observed.
– Peak AOD values alternated between summer, autumn, and winter seasons.
Correlation Analysis:
– Weak positive correlation between temperature and AOD was noted.
– Very weak negative correlation between relative humidity and AOD was observed.
Impact and Statistics:
– Air pollution is linked to millions of premature deaths globally.
– PM 2.5 μm (for those with diameters that are equal to or below 2.5 μm) exposure is associated with increased risks of lung diseases and mortality.
EPA Criteria Pollutants:
– Particulate matter (PM) is identified as having significant detrimental impact on human health and the climate.
– Long-term exposure to PM 2.5 μm is linked to increased risks of diseases and mortality.
Global Burden of Diseases:
– WHO estimates 99% of the world’s population resided in areas with poor air quality.
– Premature deaths due to ambient air pollution reached 4.2 million globally in 2019.
Economic Impact:
– Air pollution is a contributor to the global burden of diseases and impacts the economy.
– In India, air pollution caused around 1.67 million premature deaths in 2019.
Ground-Based Monitoring Stations:
– Ground-based monitoring stations have lower spatial resolution and discontinuous spatial coverage.
– Studies suggest supplementing ground-based data with global aerosol observation data from sources like satellites.
Aerosols Definition and Sources:
– Aerosols are suspensions of small particles of solids and liquids in an air medium.
– Natural sources of aerosols include seas, deserts, wildfires, and volcanoes, while anthropogenic sources mainly consist of traffic, industries, and household emissions.
Radiative Effects of Aerosols:
– Aerosols can affect the Earth’s overall radiation budget by scattering or absorbing incoming solar radiation.
– Their radiative effect can negatively affect the amount of precipitation over a region.
Correlation Studies on Aerosols:
– Several studies investigated the correlation between PM 2.5 μm concentrations and aerosols, establishing a linear correlation between the two variables.
– However, there are limitations to this relationship, as AOD is influenced by water vapour and coarser particles.
Trends in Aerosols:
– Studies investigated the trends in aerosols in various regions, showing decreasing trends in some areas and increasing trends in others.
– Some studies compared the variations in AOD and PM 2.5 μm on seasonal scales, revealing different patterns.
Air Pollution in India:
– India was ranked fifth in the list of the most-polluted countries by the WHO in 2019 based on PM 2.5 μm emissions.
– High levels of pollution, especially particulate matter, have been significantly contributing to disease burden and mortality.
Need for Research:
– The high pollution levels in India call for the development of trends for pollutants to analyze the pollution scenario and devise appropriate strategies and long-term solutions.
– There are gaps in research on long-term aerosol data analysis, with few studies employing advanced methods capable of handling auto-correlations and outliers.
Pollution in India: WHO Report:
– India was ranked fifth in the list of the most-polluted countries by the WHO in 2019 based on PM 2.5 μm emissions.
– High levels of pollution, especially particulate matter, have been significantly contributing to disease burden and mortality.
Long-Term Trends in Aerosol Concentrations:
– Pioneering investigation into long-term trends of aerosols and particulate matter in Hyderabad district using high-resolution MODIS sensor data.
– Integration of advanced statistical techniques for trend analysis set a new benchmark for identifying trends in aerosol levels.
Seasonal Variability in Aerosol Concentrations:
– Exploration of seasonal variability in peak AOD values over the years providing insights into aerosol distribution across different climatic seasons.
– Shedding light on previously unexamined patterns in the area’s air quality.
Impacts of Aerosols on Climate:
– Significant impact of aerosols on surface temperature fluctuations and precipitation suppression in a given area.
– Importance of creating aerosol prediction models and researching long-term patterns in aerosol concentrations.
Study Objectives:
– Analyze time series data of AOD and study variations in aerosol concentration.
– Identify significant long-term trends in aerosol concentration and study relationship between AOD and meteorological factors.
Study Area – Hyderabad:
– Description of Hyderabad district’s location and climate characteristics.
– Discussion on seasonal classification and air quality deterioration in Hyderabad.
Data and Methodology:
– Collection and pre-processing of AOD, meteorological, and PM 2.5 μm concentration data for trend analysis.
– Detailed explanation of methodology used including trend analysis and factor studies.
Data Collection:
– Usage of AOD values and meteorological data for Hyderabad district from January 2002 to February 2021.
– Extraction of daily mean AOD data from MODIS sensors and processing through MAIAC algorithm.
MCD19A2 Product Data:
– Extraction of relevant AOD data using MODIS sensors aboard Terra and Aqua satellites.
– Utilization of green band with a wavelength of 0.55 μm for AOD models and validation.
Data Collection:
– Daily average data collected for two locations in Hyderabad district: Secundarabad and Charminar.
– Parameters collected include relative humidity, rainfall, temperature, wind speed, and wind direction.
Fire-Count Data:
– Total fire-count data in and around the study area obtained from NASA’s FIRMS portal for 2016-2022 period.
– Used for correlation studies and analysis.
Data Pre-Processing:
– Pre-processing done using ‘pandas’ and ‘numpy’ libraries in Python.
– Checked for null values, outliers, and re-scaled AOD values.
Trend Analysis – Mann-Kendall Test:
– Mann-Kendall test used for analyzing time-series data for trends.
– Non-parametric test independent of data distribution, determines significant trends.
Trend Analysis – Sen’s Slope-Estimator Test:
– Calculates the median value of slopes between data points for trend rate estimation.
– Provides insights into the rate of change in trends detected by Mann-Kendall test.
Trend Analysis – Modified Mann-Kendall Test:
– Accounts for serial autocorrelation in data sample to avoid reporting false trends.
– Utilizes effective sample size to update variance of Mann-Kendall S statistic.
Statistical Parameters:
– Mann-Kendall Z value calculated based on differences between data points.
– P-value used to identify significance of trend based on predefined confidence level.
Conclusion:
– Significant trends identified via Mann-Kendall test, with the rate of change determined by Sen’s slope-estimator test.
– Modified Mann-Kendall test used to correct for serial autocorrelation in trend analysis.
– Naresh Nunna
Read the Original Study – https://bit.ly/4cvTJW6