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Explora: Environment
and Resource Air pollution mitigation technologies

3.12. Plastic waste disposal and recycling 3.15. Sensor and monitoring systems

Plastic is recognized as a significant contributor to air Low-cost sensors provide a practical approach for
pollution. Most plastic products are single-use and evaluating air pollutants in urban settings. However,
cannot be reused. While biodegradable plastics have they can sometimes produce inaccurate readings due to
been developed as eco-friendly alternatives, their environmental factors. The integration of machine learning
limited availability reduces their overall impact. Several and advanced computational techniques can help overcome
strategies exist for plastic waste disposal. At present, a these limitations. In addition, calibration methods can be
large proportion of plastic waste is sent to landfills, which used to ensure accurate operation under extreme weather
has negative environmental impacts. Thermal treatment conditions. Intelligent calibration systems allow sensors to
offers an alternative, as the heat generated during waste function reliably even in challenging environments, such as
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incineration can be utilized for other purposes. Recycling those with heavy smoke. Calibration results have shown
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is another viable option; however, it is constrained that these sensors can measure aerosol mass accurately.
by factors such as polymer impurities and economic Two primary types of calibrators are used: (i) White-box
feasibility. Contamination of the plastic stream can disrupt calibrators and (ii) black-box calibrators. 20
the recycling process, and when economic returns are Black-box calibrators generally outperform white-
minimal, recycling becomes impractical. 13 box calibrators, although their performance may vary
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3.13. Air pollution predictor system development depending on environmental conditions. Advanced
monitoring systems can be developed by integrating wireless
Air pollution affects regions worldwide, and the ability sensor network technology with building information
to predict pollutant concentrations is essential for timely modeling. Previous research has demonstrated that the
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countermeasures. Several predictive systems have been integration of these two technologies provides accurate
developed, with advanced models increasingly built on monitoring, as shown in tests conducted over distances of
deep learning frameworks. One such approach employs up to 250 m. However, signal strength was decreased by
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recurrent neural networks in combination with particle 15–20% when the receiver was rotated by 90° 23 and by
swarm optimization algorithms. The prediction process 30–40% when penetrating thick walls. 21
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generally involves several steps. First, air quality data are
collected from multiple monitoring stations. Second, 4. Non-technical strategies
the data are processed and prepared for analysis. Third, 4.1. Public awareness and education
the dataset is divided into training and testing subsets
according to established principles. The predictive model Public awareness campaigns play an important role
is then trained to interpret patterns from the collected in encouraging behaviors that reduce air pollution,
data and generate forecasts. Although data are typically such as reducing private vehicle use, promoting public
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collected over 30 days, using 25 days of data for model transportation, preventing open burning, and supporting
input has been demonstrated to yield optimal results. 18 clean energy initiatives. These campaigns should focus on
educating individuals about the sources and health effects
3.14. Fuel quality improvements of air pollution while fostering community-level action.

China produces the largest number of automobiles globally Several case studies illustrate the impact of awareness
and is also among the world’s most heavily polluted initiatives. For example, Mahajan et al. reported that
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countries. Faulty vehicles and low-quality gasoline are involving approximately 400 citizens in air quality
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major contributors to urban air pollution. Implementing monitoring increased awareness of PM and NO
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2.5
2
strict fuel standards is a viable strategy for reducing pollutant exposure risks and led to a 21% reduction in car usage
emissions. For example, improvements in gasoline quality among participants in European urban areas. Similarly, a
in China have resulted in a 12.9% reduction in pollutant survey of 800 citizens in Isfahan by Jokar et al. found that
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emissions and an estimated financial benefit of USD higher environmental awareness significantly predicted
26 billion. Under the new standards, the sulfur content of pro-environmental behavior (p<0.01), with educational
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fuel is significantly reduced. Biofuels also offer a promising interventions improving willingness to adopt cleaner
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solution for reducing air pollution. Each type of biofuel practices by 30%. In Spain, Sánchez-García et al. found
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provides specific benefits; for instance, oils derived from that among 1200 respondents, 68% were willing to pay
lemon peel and orange peel have been identified as suitable for policies aimed at reducing traffic-related air pollution.
biofuel feedstocks. Research indicates that blending 20% The average willingness to pay was EUR 3.95/month per
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biofuel with conventional diesel can be used effectively in person, highlighting the connection between awareness
traditional diesel engines without major modifications. 14 and economic support for pollution control policies.
Volume 2 Issue 3 (2025) 7 doi: 10.36922/EER025210041