A bird's eye view of the air pollution-cancer link in China

The association between gaseous pollutants and non-accidental mortality: a time series study

To evaluate the effects of gaseous pollutants (SO₂, NO₂) on non-accidental mortality of residents in Hefei city, we collected non-accidental deaths, air pollutants and meteorological data of Hefei city from 2014 to 2017. After controlling confounding factors with Poisson generalized additive model, we analyzed the relationship between air pollutants and non-accidental mortality and used subgroup analysis to identify susceptible groups. The number of non-accidental deaths during the study period was 42,116, with an average of 28.83 per day. The average concentrations of SO₂ and NO₂ were 16.08 μg/m³ and 39.10 μg/m³, respectively. In the single-pollutant model, every 10 μg/m³ increase in SO₂ and NO₂ concentrations was significantly associated with non-accidental mortality, and there was a significant lag effect. SO₂ increased the risk of non-accidental death by 4.93% (95% CI: 1.94% ~ 8.00%) at lag0-3. In addition, male, the elderly, non-elderly and low-education people were more susceptible (P < 0.05). NO₂ increased the risk of non-accidental death by 2.11% (95% CI: 1.18% ~ 3.05%) at lag0-1 and had an effect on all subgroups (P < 0.05). For every 10 μg/m³ increase in SO₂ and NO₂, the two-pollutant model showed that the risk of non-accidental death, respectively, increased by 3.34% (95% CI: 0.29% ~ 6.50%) and 1.82% (95% CI: 0.85% ~ 2.79%), suggesting that the effect was weakened. Our study suggested that SO₂ and NO₂ were associated with non-accidental mortality, and there were lag effects. Therefore, environmental management should be strengthened and health protection education should be carried out for different groups.

A Short-Term Prediction Model of PM2.5 Concentration Based on Deep Learning and Mode Decomposition Methods

Based on a set of deep learning and mode decomposition methods, a short-term prediction model for PM2.5 concentration for Beijing city is established in this paper. An ensemble empirical mode decomposition (EEMD) algorithm is first used to decompose the original PM2.5 timeseries to several high- to low-frequency intrinsic mode functions (IMFs). Each IMF component is then trained and predicted by a combination of three neural networks: back propagation network (BP), long short-term memory network (LSTM), and a hybrid network of a convolutional neural network (CNN) + LSTM. The results showed that both BP and LSTM are able to fit the low-frequency IMFs very well, and the total prediction errors of the summation of all IMFs are remarkably reduced from 21 g/m3 in the single BP model to 4.8 g/m3 in the EEMD + BP model. Spatial information from 143 stations surrounding Beijing city is extracted by CNN, which is then used to train the CNN+LSTM. It is found that, under extreme weather conditions of PM2.5 < 35 g/m3 and PM2.5 > 150 g/m3, the prediction errors of the CNN + LSTM model are improved by ~30% compared to the single LSTM model. However, the prediction of the very high-frequency IMF mode (IMF-1) remains a challenge for all neural networks, which might be due to microphysical turbulences and chaotic processes that cannot be resolved by the above-mentioned neural networks based on variable–variable relationship.

One Health in China

As a result of rapid economic growth over the previous three decades, China has become the second largest economy worldwide since 2010. However, as a developing country with the largest population, this rapid economic growth primarily based on excessive consumption and waste of resources. Thus, China has been facing particularly severe ecological and environmental problems in speeding up industrialization and urbanization. The impact of the health risk factors is complex and difficult to accurately predict. Therefore, it is critical to investigate potential threats in the context of the human-animal-environment interface to protect human and animal health. The "One Health" concept recognizes that human health is connected to animal and environmental health. This review primarily discusses specific health problems in China, particularly zoonoses, and explains the origin and development of the One Health approach, as well as the importance of a holistic approach in China.

Global efforts in conquering lung cancer in China

Lung cancer, the most prevalent and deadly malignancy in the world, poses a particularly critical healthcare challenge to China due to the rapidly increasing new cases and the unique cancer genetics in Chinese patient population. Substantial progress has been made in molecular diagnosis and personalized treatment of the disease. The field is now moving towards multiple new directions to include (1) new generation of targeted agents such as epidermal growth factor receptor and anaplastic lymphoma kinase inhibitors to overcome resistance to their early generation counterparts; and (2) deeper understanding of tumor genetics of each individual patient and consequently the application of biomarkers to guide personalized treatment as well as novel drug development including combination therapy. The increasing capacity in innovative cancer drug research and development is supported by extensive collaboration within China and globally, and across academia and industry, to build up expertise and infrastructure in early-phase clinical testing of novel drugs. With these combined efforts, new and better medicines will be available for lung cancer patients in China in the near future.

Household air pollution and lung cancer in China: a review of studies in Xuanwei

Over half of the world's population is exposed to household air pollution from the burning of solid fuels at home. Household air pollution from solid fuel use is a leading risk factor for global disease and remains a major public health problem, especially in low- and mid-income countries. This is a particularly serious problem in China, where many people in rural areas still use coal for household heating and cooking. This review focuses on several decades of research carried out in Xuanwei County, Yunnan Province, where household coal use is a major source of household air pollution and where studies have linked household air pollution exposure to high rates of lung cancer. We conducted a series of case-control and cohort studies in Xuanwei to characterize the lung cancer risk in this population and the factors associated with it. We found lung cancer risk to vary substantially between different coal types, with a higher risk associated with smoky (i.e., bituminous) coal use compared to smokeless (i.e., anthracite) coal use. The installation of a chimney in homes resulted in a substantial reduction in lung cancer incidence and mortality. Overall, our research underscores the need among existing coal users to improve ventilation, use the least toxic fuel, and eventually move toward the use of cleaner fuels, such as gas and electricity.

Air pollution: a smoking gun for cancer

Once considered a taboo topic or stigma, cancer is the number one public health enemy in the world. Once a product of an almost untouchable industry, tobacco is indisputably recognized as a major cause of cancer and a target for anticancer efforts. With the emergence of new economic powers in the world, especially in highly populated countries such as China, air pollution has rapidly emerged as a smoking gun for cancer and has become a hot topic for public health debate because of the complex political, economic, scientific, and technologic issues surrounding the air pollution problem. This editorial and the referred articles published in this special issue of the Chinese Journal of Cancer discuss these fundamental questions. Does air pollution cause a wide spectrum of cancers? Should air pollution be considered a necessary evil accompanying economic transformation in developing countries? Is an explosion of cancer incidence coming to China and how soon will it arrive? What must be done to prevent this possible human catastrophe? Finally, the approaches for air pollution control are also discussed.