Spatiotemporal Characterization of Ambient PM_2.5 Concentrations in Shandong Province (China)

China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space–time variability of PM_2.5 concentrations and predict their distribution in the Shandong province, China. Daily PM_2.5 concentrations obtained at air quality monitoring sites during 2014 were used. On the basis of the space–time PM_2.5 distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM_2.5 exceeded 75, 115, 150, and 250 μg m^–3 varied: 43–253, 13–128, 4–66, and 0–15 days, respectively. The PM_2.5 pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM_2.5 exceeded 150 μg m^–3 during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM_2.5 pollution intensity and space–time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM_2.5 concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold

Urban-Rural Disparity of Breast Cancer and Socioeconomic Risk Factors in China

<div><p>Breast cancer is one of the most commonly diagnosed cancers worldwide. The primary aim of this work is the study of breast cancer disparity among Chinese women in urban vs. rural regions and its associations with socioeconomic factors. Data on breast cancer incidence were obtained from the Chinese cancer registry annual report (2005–2009). The ten socioeconomic factors considered in this study were obtained from the national population 2000 census and the Chinese city/county statistical yearbooks. Student’s T test was used to assess disparities of female breast cancer and socioeconomic factors in urban vs. rural regions. Pearson correlation and ordinary least squares (OLS) models were employed to analyze the relationships between socioeconomic factors and cancer incidence. It was found that the breast cancer incidence was significantly higher in urban than in rural regions. Moreover, in urban regions, breast cancer incidence remained relatively stable, whereas in rural regions it displayed an annual percentage change (APC) of 8.55. Among the various socioeconomic factors considered, breast cancer incidence exhibited higher positive correlations with population density, percentage of non-agriculture population, and second industry output. On the other hand, the incidence was negatively correlated with the percentage of population employed in primary industry. Overall, it was observed that higher socioeconomic status would lead to a higher breast cancer incidence in China. When studying breast cancer etiology, special attention should be paid to environmental pollutants, especially endocrine disruptors produced during industrial activities. Lastly, the present work’s findings strongly recommend giving high priority to the development of a systematic nationwide breast cancer screening program for women in China; with sufficient participation, mammography screening can considerably reduce mortality among women.</p></div

Descriptive analysis of socioeconomic factors in urban vs. rural regions.

<p>PD: population density, PNA: percentage of non-agriculture population, SIO: second industry output, PET: percentage of population employed in tertiary industry, EDU: average years of education, PEP: percentage of population employed in primary industry, PU: percentage of unemployed population (unemployment rate), PES: percentage of population employed in second industry, PI: percentage of illiteracy (illiteracy rate), PIO: primary industry output.</p><p>* Correlation is significant at the 0.05 level (2-tailed).</p><p>** Correlation is significant at the 0.01 level (2-tailed).</p><p>Descriptive analysis of socioeconomic factors in urban vs. rural regions.</p

Female breast cancer incidence in Chinese urban <i>vs</i>. rural regions from 2005 to 2009.

<p>Female breast cancer incidence in Chinese urban <i>vs</i>. rural regions from 2005 to 2009.</p

Geographic distribution of the cancer registries in China.

<p>Geographic distribution of the cancer registries in China.</p

Ordinary Least Squares (QLS) model for socioeconomic factors and breast cancer incidence.

<p>PD: population density, PNA: percentage of non-agriculture population, SIO: second industry output, PET: percentage of population employed in tertiary industry, EDU: average years of education, PEP: percentage of population employed in primary industry, PU: percentage of unemployed (unemployment rate), PES: percentage of population employed in second industry, PI: percentage of illiteracy (illiteracy rate), PIO: primary industry output, BC: Breast cancer incidence.</p><p>** Correlation is significant at the 0.01 level (2-tailed).</p><p>Ordinary Least Squares (QLS) model for socioeconomic factors and breast cancer incidence.</p

Female breast cancer incidence, urban vs. rural (U/R).

<p>U: urban city, R: rural city, Incidence: 4-year average breast cancer incidence (1/100,000), CI: confidence interval.</p><p>Female breast cancer incidence, urban vs. rural (U/R).</p

Pearson’s correlation coefficients between socioeconomic factors and breast cancer incidence.

<p>PD: population density, PNA: percentage of non-agriculture population, SIO: second industry output, PET: percentage of population employed in tertiary industry, EDU: average years of education, PEP: percentage of population employed in primary industry, PU: percentage of unemployed (unemployment rate), PES: percentage of population employed in second industry, PI: percentage of illiteracy (illiteracy rate), PIO: primary industry output, BC: Breast cancer incidence.</p><p>** Correlation is significant at the 0.01 level (2-tailed).</p><p>Pearson’s correlation coefficients between socioeconomic factors and breast cancer incidence.</p

Comparison of breast cancer incidence in urban vs. rural regions (N = 31).

<p>U: urban city, R: rural city, N: the number of cities in urban/rural areas, incidence (1/100,000), STD: standard deviation, CI: confidence interval. <i>P</i>: significance of difference of incidence between urban and rural areas.</p><p>Comparison of breast cancer incidence in urban vs. rural regions (N = 31).</p

Female breast cancer incidence in different age groups during 2005–2009^a.

<p>^a Age groups: A: 0–24, B: 25–29, C: 30–34, D: 35–39, E: 40–44, F: 45–49, G: 50–54, H: 55–59, I: 60–64, J: 65–69, K: 70–74, L: 75–79, M: 80–84, N: > 85.</p