Search Results for « Urban air quality »

Urban air quality management plan (UAQMP) is an effective and efficient tool employed in managing acceptable urban air quality. However, the UAQM practices are specific to a country’s needs and requirements. Majority of the developed countries have full–fledged UAQMP with a regulatory management framework. However, developing countries are still working in formulating the effective and efficient UAQMPs to manage their deteriorating urban air environment. The first step in the process of formulation of UAQMP is to identify the air quality control regions based on ambient air quality status and second, initiate a time bound program involving all stakeholders to develop UAQMPs. The successful implementation of UAQMPs depends on the strength of its key components, e.g. goal/objective, monitoring network, emission inventory, air quality modeling, control strategies and public participation. This paper presents a comprehensive review on UAQMPs, being implemented worldwide at different scales e.g., national (macro), city (medium), and local (micro).

As evidence for the devastating impacts of air pollution on human health continues to increase, improving urban air quality has become one of the most pressing tasks facing policy makers world-wide. Increasingly, and very often on the basis of conflicting and/or weak evidence, the introduction of green infrastructure (GI) is seen as a win-win solution to urban air pollution, reducing ground-level concentrations without imposing restrictions on traffic and other polluting activities. The impact of GI on air quality is highly context dependent, with models suggesting that GI can improve urban air quality in some situations, but be ineffective or even detrimental in others. Here we set out a novel conceptual framework explaining how and where GI can improve air quality, and offer six specific policy interventions, underpinned by research, that will always allow GI to improve air quality. We call GI with unambiguous benefits for air quality GI4AQ. However, GI4AQ will always be a third-order option for mitigating air pollution, after reducing emissions and extending the distance between sources and receptors.

This study assessed the urban air quality in 16 large Indonesian cities on the islands of Java, Sumatra, Kalimantan, Sulawesi, Maluku, and Papua from 2010 till 2017. 24-h samples of airborne particulate matter (PM) in two size fractions, PM2.5 (< 2.5 µm in aerodynamic diameter) and PM2.5-10 (2.5–10 µm in aerodynamic diameter), were collected weekly using a Gent stacked filter unit sampler and then analyzed for their mass concentrations, black carbon (BC) content, and elemental compositions. The majority of the average annual PM2.5 concentrations measured at the Java sites (Bandung, Jakarta, Semarang, and Surabaya) exceeded the Indonesian annual ambient air quality standard (15 µg m–3), although the other tested locations, excluding Pekanbaru and Palangka Raya, exhibited values below the standard. During the forest fire episodes of 2015, the average daily PM2.5 concentrations in Pekanbaru and Palangka Raya rose above the national daily ambient standard (65 µg m–3). The percentage of BC, which is associated with traffic emission and biomass burning, averaged between 15% and 26% (a significant fraction) in the PM2.5. The concentrations of the major elements in the PM2.5, viz., Si, S, K, Fe, Zn, and Pb, varied widely from site to site, although all of the locations displayed enhanced levels of the crustal elements Si and S, which originated from unpaved roads and volcanic eruptions, and vehicle fuel, forest fires, and volcanic emissions, respectively. Significantly higher concentrations of heavy metals (Fe, Zn, and Pb in Surabaya and Pb in Tangerang) were found at the heavily industrialized sites, demonstrating the effect of local industrial emissions on air quality. Our results, which are based on a crucial survey of PM concentrations and compositions in Indonesia, provide a scientific basis for developing and improving various air quality policies in the nation, including an early warning system for severe pollution events.

China is undergoing rapid urbanization because of unprecedented economic growth. As a result, many cities suffer from air pollution. Two-thirds of China's cities have not attained the ambient air quality standards applicable to urban residential areas (Grade II). Particulate matter (PM), rather than sulfur dioxide (SO2), is the major pollutant reflecting the shift from coal burning to mixed source pollution. In 2002, 63.2 and 22.4% of the monitored cities have PM and SO2 concentrations exceeding the Grade II standard, respectively. Nitrogen oxides (NOx) concentration kept a relatively stable level near the Grade II standard in the last decade and had an increasing potential in recent years because of the rapid motorization. In general, the air pollutants emission did not increase as quickly as the economic growth and energy consumption, and air quality in Chinese cities has improved to some extent. Beijing, a typical representative of rapidly developing cities, is an example to illustrate the possible options for urban air pollution control. Beijing's case provides hope that the challenges associated with improving air quality can be met during a period of explosive development and motorization.

The paper did not originally have an abstract. It explores the robustness of hedonic pricing estimation, focusing on four areas: 1) variable selection and treatment, 2) measurement error, 3) functional form, and 4) error distribution. The paper offers insights to guide the future conduct of hedonic environmental benefit studies.

Low-cost urban air quality sensor networks are increasingly used to study the spatio-temporal variability in air pollutant concentrations. Recently installed low-cost urban sensors, however, are more prone to result in erroneous data than conventional monitors, e.g., leading to outliers. Commonly applied outlier detection methods are unsuitable for air pollutant measurements that have large spatial and temporal variations as occur in urban areas. We present a novel outlier detection method based upon a spatio-temporal classification, focusing on hourly NO 2 concentrations. We divide a full year’s observations into 16 spatio-temporal classes, reflecting urban background vs. urban traffic stations, weekdays vs. weekends, and four periods per day. For each spatio-temporal class, we detect outliers using the mean and standard deviation of the normal distribution underlying the truncated normal distribution of the NO 2 observations. Applying this method to a low-cost air quality sensor network in the city of Eindhoven, the Netherlands, we found 0.1–0.5% of outliers. Outliers could reflect measurement errors or unusual high air pollution events. Additional evaluation using expert knowledge is needed to decide on treatment of the identified outliers. We conclude that our method is able to detect outliers while maintaining the spatio-temporal variability of air pollutant concentrations in urban areas.

We present a review of the air quality in Finnish cities and towns, based on the data from urban measurement networks. The data were compiled from 42 Finnish cities and towns, from measurements conducted in 1990–1993. These data show that the newly-introduced (1996) national air quality guidelines have been fairly often exceeded in urban areas, most commonly for particulate matter, both PM10 and total suspended particles (TSP). Some exceedances have also occurred for NO2 and CO, at sites with high traffic densities. Some TSP values in the largest cities in Finland are exceptionally high, caused by the resuspension of dust from street surfaces. However, the European Union air quality limit values were only exceeded at one measurement station for TSP in 1990, 1991 and 1992. We also analysed seasonal variation of the guideline exceedings.