1. Using a low-cost monitor to assess the impact of leaf blowers on particle pollution during street cleaning
- Author
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Daniel Bañón, J.A. Moreno, Isabel Costa-Gómez, Belén Elvira-Rendueles, Stella Moreno-Grau, and Raquel Revuelta
- Subjects
Pollution ,Atmospheric Science ,010504 meteorology & atmospheric sciences ,Health, Toxicology and Mutagenesis ,media_common.quotation_subject ,Environmental engineering ,Sampling (statistics) ,010501 environmental sciences ,Management, Monitoring, Policy and Law ,Particulates ,01 natural sciences ,Street cleaning ,Particle ,Environmental science ,Air quality index ,Particle counter ,0105 earth and related environmental sciences ,Cardiopulmonary disease ,media_common - Abstract
Personal exposure to particulate matter (PM) is associated with a variety of adverse health effects and cardiopulmonary diseases. As a mitigating measure to improve air quality, policymakers should select street cleaning tools according to their potential environmental impact, but there is little information about their actual effect on particle pollution. This paper describes the contribution made by leaf blowers to suspended PM and analyzes the duration of this effect during street sweeping in an urban environment. Particle concentration has been monitored throughout a fixed-site 104-day sampling campaign using the Dylos DC1700, a low-cost real-time laser particle counter. This detector recognizes two sizes of particles, coarse and fine, and records data every minute, which provides unique time resolution in the observation of the effect of leaf blowers. The results show that the use of leaf blowers raises fine PM to 13.9 μg/m3 and coarse ones to 31.5 μg/m3, which increases the number 1.6 times and 1.7 times, respectively, when compared with normal median particle concentration. The particulate matter stays resuspended in the air for several minutes, creating a dust wave effect. Low-cost sensors, such as the Dylos, are proposed as a practical methodology to help local decision-makers incorporate environmental variables in decision-making.
- Published
- 2019