Spatial saturation studies using source-specific chemical tracers are commonly used to examine intra-urban variation in exposures and source impacts, for epidemiology and policy purposes. Most such studies, however, has been performed in North America and Europe, with substantial regional combustion-source contributions. In contrast, Auckland, New Zealand, a large western city, is relatively isolated in the south Pacific, with minimal impact from long-range combustion sources. However, fluctuating wind patterns, complex terrain, and an adjacent major port complicate pollution patterns within the central business district (CBD). We monitored multiple pollutants (fine particulate matter (PM_2.5), black carbon (BC), elemental composition, organic diesel tracers (polycyclic aromatic hydrocarbons (PAHs), hopanes, steranes), and nitrogen dioxide (NO₂)) at 12 sites across the ~5 km² CBD during autumn 2014, to capture spatial variation in traffic, diesel, and proximity to the port. PM_2.5 concentrations varied 2.5-fold and NO₂ concentrations 2.9-fold across the CBD, though constituents varied more dramatically. The highest-concentration constituent was sodium (Na), a distinct non-combustion-related tracer for sea salt (µ = 197.8 ng/m³ (SD = 163.1 ng/m³)). BC, often used as a diesel-emissions tracer, varied more than five-fold across sites. Vanadium (V), higher near the ports, varied more than 40-fold across sites. Concentrations of most combustion-related constituents were higher near heavy traffic, truck, or bus activity, and near the port. Wind speed modified absolute concentrations, and wind direction modified spatial patterns in concentrations (i.e., ports impacts were more notable with winds from the northeast).

This study employs the correlation coefficients technique to support emission sources detection for indoor environments. Unlike existing methods analyzing merely primary pollution, we consider alternatively the secondary pollution (i.e., chemical reactions between pollutants in addition to pollutant level), and calculate intra pollutants correlation coefficients for characterizing and distinguishing emission events. Extensive experiments show that seven major indoor emission sources are identified by the proposed method, including (1) frying canola oil on electric hob, (2) frying olive oil on an electric hob, (3) frying olive oil on a gas hob, (4) spray of household pesticide, (5) lighting a cigarette and allowing it to smoulder, (6) no activities, and (7) venting session. Furthermore, our method improves the detection accuracy by a support vector machine compared to without data filtering and applying typical feature extraction methods such as PCA and LDA.

The authors’ project O-Tū-Kapua (what clouds see) explored concepts related to air quality by creating a mixed reality, art/science, educationally focused exhibition for children, mixing participatory art, scientific concepts and technology. The young are directly affected by the social and environmental impacts of the changing world climate, making their voices particularly important in determining ongoing dialogue about Earth's atmosphere. Through a nexus of the handmade, education, augmented technology, installation and aural soundscapes, O-Tū-Kapua engaged over 1,000 children. The project also combined real-time air quality and weather data, superimposing it as a virtual data layer on top of hand-drawn images of native flora and fauna. The magnitude of the data determined the visual and aural form of the virtual experience, creating environmentally responsive representations that enabled participants to readily see and interpret how the atmosphere was affecting the native forest in their neighborhood.