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Fine particulate matter (PM2.5) is of great concern to the public due to its significant risk to human health. Numerous methods have been developed to estimate spatial PM2.5 concentrations in unobserved locations due to the sparse number of fixed monitoring stations. Due to an increase in low-cost sensing for air pollution monitoring, crowdsourced monitoring of exposure control has been gradually introduced into cities. However, the optimal mapping method for conventional sparse fixed measurements may not be suitable for this new high-density monitoring approach. This study presents a crowdsourced sampling campaign and strategies of method selection for 100 m scale PM2.5 mapping in an intra-urban area of China. During this process, PM2.5 concentrations were measured by laser air quality monitors through a group of volunteers during two 5 h periods. Three extensively employed modelling methods (ordinary kriging, OK; land use regression, LUR; and regression kriging, RK) were adopted to evaluate the performance. An interesting finding is that PM2.5 concentrations in micro-environments varied in the intra-urban area. These local PM2.5 variations can be easily identified by crowdsourced sampling rather than national air quality monitoring stations. The selection of models for fine-scale PM2.5 concentration mapping should be adjusted according to the changing sampling and pollution circumstances. During this project, OK interpolation performs best in conditions with non-peak traffic situations during a lightly polluted period (holdout validation R2: 0.47–0.82), while the RK modelling can perform better during the heavily polluted period (0.32–0.68) and in conditions with peak traffic and relatively few sampling sites (fewer than ∼100) during the lightly polluted period (0.40–0.69). Additionally, the LUR model demonstrates limited ability in estimating PM2.5 concentrations on very fine spatial and temporal scales in this study (0.04–0.55), which challenges the traditional point about the good performance of the LUR model for air pollution mapping. This method selection strategy provides empirical evidence for the best method selection for PM2.5 mapping using crowdsourced monitoring, and this provides a promising way to reduce the exposure risks for individuals in their daily life.

Chenxia Hu,1 Ke Yang,1 Mengjie Li,1 Weiping Huang,2 Fengxue Zhang,2 Hongqi Wang2 1School of Pharmaceutical Science, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China; 2The Research Center of Basic Integrative Medicine, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China Abstract: Although systematic therapeutic approaches have reduced cancer-associated mortality, metastatic breast cancer can still evade therapy, particularly triple-negative breast cancer, which remains associated with high rates of cancer metastasis and has the worst clinical prognosis. Lipocalin 2 (LCN2) is a secreted glycoprotein that transports small lipophilic ligands. Its abnormal expression serves critical roles in the epithelial-to-mesenchymal transition process, angiogenesis, and cell migration and invasion in breast cancer. Notably, LCN2 functions as an initiator of carcinogenesis and metastasis by involving multiple signaling pathways. The present review aims to summarize research findings on the abnormal expression of LCN2 in breast cancer progression. Furthermore, the review highlights the latest developments of potential LCN2-targeting agents and proposed LCN2-associated molecular mechanisms with regard to breast cancer invasion and metastasis. Keywords: lipocalin 2, breast cancer, metastasis, triple-negative breast cancer, epithelial-to-mesenchymal transition, angiogenesis