ABSTRACT
Concentrations of ambient particulate matter (PM) depend on various factors including emissions of primary pollutants, meteorology and chemical transformations. New Delhi, India is the most polluted megacity in the world and routinely experiences extreme pollution episodes. As part of the Delhi Aerosol Supersite study, we measured online continuous PM1 (particulate matter of size less than 1µm) concentrations and composition for over five years starting January 2017, using an Aerosol Chemical Speciation Monitor (ACSM). Here, we describe the development and application of machine learning models using random forest regression to estimate the concentrations, composition, sources and dynamics of PM in Delhi. These models estimate PM1 species concentrations based on meteorological parameters including ambient temperature, relative humidity, planetary boundary layer height, wind speed, wind direction, precipitation, agricultural burning fire counts, solar radiation and cloud cover. We used hour of day, day of week and month of year as proxies for time-dependent emissions (e.g., emissions from traffic during rush hours). We demonstrate the applicability of these models to capture temporal variability of the PM1 species, to understand the influence of individual factors via sensitivity analyses, and to separate impacts of the COVID-19 lockdowns and associated activity restrictions from impacts of other factors. Our models provide new insights into the factors influencing ambient PM1 in New Delhi, India, demonstrating the power of machine learning models in atmospheric science applications. [ABSTRACT FROM AUTHOR] Copyright of Aerosol Science & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
Mentor- mentee relationship in any discipline is a professional and interpersonal relationship. It associates a mentor with a protégé or a mentee. Mentoring is a serious business in Ophthalmology, both academically and surgically. The mentors act as role models for future generations by acting as a friend, coach, or guide to the mentee. They do so by giving valuable advice, moral support, and inculcating skills in a mentee. It is difficult to pinpoint the precise function of the mentor-mentee relationship, but the final goal is to achieve personal and professional objectives. In the current article, the authors have shed light on the imperative aspect of one's Ophthalmology career, i.e., the mentor-mentee relationship. This article describes various aspects of mentoring, the traits of a perfect mentor and mentee, the pre-requisites for a good mentee-mentor relationship, the hindrances and obstacles in a good relationship, and the impact of COVID-19 on the same. The potential goal of this article is to ignite the constructive spirit of the mentor-mentee relationship, encourage potential mentors to become ideal mentors, and potential mentees to gain from serious mentors.