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Background: Street sweepers play an important role in maintaining the health and hygiene within the cities. This job exposes the street sweepers to a variety of risk factors such as dust, toxins and diesel exhaust pollution, which make them vulnerable to develop certain occupational diseases. Therefore, it was thought necessary to study the morbidity profile in this occupational group.
Objectives: To study the prevalence of morbidities among street sweepers and comparison group.
Study Design: A cross-sectional study with a comparison group.
Study Setting: Nagpur Municipal Corporation, Nagpur.
Subjects: THE STUDY INCLUDED TWO GROUPS: (1) A study group comprising 273 street sweepers. (2) A comparison group comprising 142 class IV workers working in the office buildings of Nagpur Municipal Corporation, Nagpur.
Materials And Methods: A pretested proforma was used to record the necessary information such as clinical history, sociodemographic factors, findings of clinical examination and investigations performed.
Results And Conclusions: THE IMPORTANT MORBIDITIES DETECTED AMONG STREET SWEEPERS WERE THE FOLLOWING: anemia (20.5%), hypertension (9.5%), upper respiratory tract infections (URTI) (7.3%) and chronic bronchitis (5.9%). In the comparison group, important morbidities detected were the following: anemia (20.4%), hypertension (11.3%), hyperacidity (9.9%), URTI (7.0%) and refractive error (7.0%). Chronic bronchitis was detected in two subjects (1.4%) of the comparison group. The prevalence of chronic bronchitis was significantly high among street sweepers than that of subjects of the comparison group. Therefore, it is recommended that further studies with a larger sample size be undertaken to identify the factors responsible for higher prevalence of chronic bronchitis among the street sweepers.
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http://dx.doi.org/10.4103/0970-0218.43226 | DOI Listing |
Environ Pollut
July 2025
School of the Environment, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada; Department of Physical and Environmental Sciences, University of Toronto (Scarborough), Toronto, Ontario, Canada. Electronic address: clare
To better link airborne PM exposures with toxic endpoints, measurements of oxidative potential (OP) could be an important metric to supplement existing mass-based approaches. The OP of airborne PM has been previously examined elsewhere using acellular chemical assays. The OP of road dust, which is an airborne source of metal(loid)s capable of inducing the generation of reactive oxygen species (ROS), has yet to be characterized.
View Article and Find Full Text PDFPLoS One
April 2025
Department of Environmental Health, College of Medicine and Health Sciences, Debre Markos University, Debre Markos, Ethiopia.
In low- and middle-income countries, occupational exposure continues to be a serious public health concern. Because of their working conditions, street sweepers are particularly vulnerable to health hazards, with respiratory issues being the most common. The lack of comprehensive and inconsistence evidence on occupational respiratory symptoms among street sweepers, which exacerbates this issue.
View Article and Find Full Text PDFEnviron Pollut
May 2025
Southern Ontario Centre for Atmospheric Aerosol Research, University of Toronto, 200 College Street, Toronto, Ontario, M5S 3E5, Canada.
Non-tailpipe emissions have gained growing attention as an emerging source of traffic-related air pollution, especially as tailpipe emissions decline. This study conducted extensive mobile measurements in a high-density urban area over two years to investigate the spatial variability of resuspended road dust and evaluate the real-world effectiveness of street sweeping. Resuspended particulate matter (PM), specifically PM and PM, was measured alongside pollutants from tailpipe and non-tailpipe sources.
View Article and Find Full Text PDFSci Rep
March 2025
Shaanxi Transportation Holding Group Co., Ltd, Xi'an, 710075, China.
The research examines the challenges city street sweepers face, which struggles to adapt cleaning settings based on varying road garbage volume, resulting in inefficient cleaning and high energy consumption. The study proposes a fuzzy control algorithm for adjusting the cleaning parameters of street sweepers based on road garbage volume grading. It starts by utilizing the YOLO (You Only Look Once) v5 deep learning model for target detection and garbage classification on road surfaces.
View Article and Find Full Text PDF