Respiratory illness and air pollution from the steel industry: the case of Piquiá de Baixo, Brazil (Preliminary report)

Source apportionment of ambient PM10 collected at three sites in an urban-industrial area with multi-time resolution factor analyses

Chemical speciation data for PM₁₀, collected for annual trend analyses of health-relevant species, at three receptor sites in a highly industrialized area (IJmond) in the Netherlands were used in a multi-time resolution receptor model (ME-2) to identify the PM₁₀ sources in this area. Despite the available data not being optimized for receptor modelling, five-factor solutions were obtained for all sites based on independent PMF analysis on PM₁₀ data from the three sites (IJM, WAZ and BEV). Four factors were common to all three sites: nitrate-sulphate (average percentage contributions to PM₁₀: IJM: 35.3 %, WAZ: 37.7 %, and BEV: 36.3 %); sea salt (20.2 %, 23.7 %, 15.2 %); industrial (8.1 %, 11.0 %, 18.1 %) and brake wear/traffic (31.4 %, 21.2 %, 20.6 %). At WAZ, a local/site-specific factor containing most of the PAH measurements was found (6.4 %) while a crustal matter factor was resolved at IJM (7.6 %) and BEV (9.8 %). Additionally, sludge-drying was a potential source of the marker species in the industrial factor at WAZ. Bootstrapping (BS) and factor displacement (DISP) were applied to the factor profiles in this work for error estimation. In general, the factor profiles at all three sites had very small intervals from both BS and DISP methods. To our knowledge, this is the first time DISP was applied in a complex model such as the multi-time resolution model. Most of the measured metal and PAH concentrations found in the IJmond area during the 2017-2019 period had local sources, with significant contributions from several processes related to the steel industry. This study shows that available detailed PM₁₀ chemical speciation data, although primarily collected for annual trend analyses of health-relevant species, could also be used in receptor modelling by applying a multi-time framework. We propose general recommendations for the optimization of the measurement strategy for source apportionment of PM in areas with similar urban-industrial land use.

The Impact of the Synergistic Effect of Temperature and Air Pollutants on Chronic Lung Diseases in Subtropical Taiwan

Previous studies have suggested an association between air pollution and lung disease. However, few studies have explored the relationship between chronic lung diseases classified by lung function and environmental parameters. This study aimed to comprehensively investigate the relationship between chronic lung diseases, air pollution, meteorological factors, and anthropometric indices. We conducted a cross-sectional study using the Taiwan Biobank and the Taiwan Air Quality Monitoring Database. A total of 2889 participants were included. We found a V/U-shaped relationship between temperature and air pollutants, with significant effects at both high and low temperatures. In addition, at lower temperatures (<24.6 °C), air pollutants including carbon monoxide (CO) (adjusted OR (aOR):1.78/Log 1 ppb, 95% CI 0.98–3.25; aOR:5.35/Log 1 ppb, 95% CI 2.88–9.94), nitrogen monoxide (NO) (aOR:1.05/ppm, 95% CI 1.01–1.09; aOR:1.11/ppm, 95% CI 1.07–1.15), nitrogen oxides (NO_x) (aOR:1.02/ppm, 95% CI 1.00–1.05; aOR:1.06/ppm, 95% CI 1.04–1.08), and sulfur dioxide (SO₂) (aOR:1.29/ppm, 95% CI 1.01–1.65; aOR:1.77/ppm, 95% CI 1.36–2.30) were associated with restrictive and mixed lung diseases, respectively. Exposure to CO, NO, NO₂, NO_x and SO₂ significantly affected obstructive and mixed lung disease in southern Taiwan. In conclusion, temperature and air pollution should be considered together when evaluating the impact on chronic lung diseases.