From the perspective of platform economics, crowdsourcing is a very efficient business model, and the pricing of crowdsourcing tasks is a key factor for the sustainable development of the crowdsourcing model. In the logistics industry, crowdsourcing provides a new idea of sustainable development for logistics enterprises, and reasonable distribution pricing is the key to achieving sustainable development. This paper innovatively adds dynamic and decentralized characteristics of logistics on the basis of a detailed analysis of pricing methods and uses this as a basis to build a pricing model. First, based on existing crowdsourced photography task pricing data, this paper establishes a project-centric domain and builds metrics into the attributes of each project based on the data in that domain. Then, a regression model is used to fit the completion rate of previous projects, and a multiple linear regression and optimal pricing mechanism are established. Finally, the DBSCAN algorithm is used to cluster areas with a high project density, and a pricing optimization model based on polynomial Logit (MNL) is established. We found through the model analysis that the optimized pricing strategy of crowdsourcing logistics services has a better packaging completion rate based on a combination of complex factors including bundling and outliers. In short, the main contributions of this paper are to build a complex mathematical model for crowdsourcing tasks, improve the algorithmic deficiencies of the previous crowdsourcing task pricing methods, and provide a reference for further research on crowdsourcing tasks. INDEX TERMS Crowdsourcing logistics, pricing optimization model, DBSCAN clustering algorithm, sustainable development.

The rapid industrialization of cities has brought many challenges to the environment and resources. Industrial wastes, automobile exhaust, coal combustion soot and other pollutants accumulate in urban soil, and the characteristics of urban soil are changed, causing many pollutants to accumulate in the urban soil environment. Heavy metals are toxic and harmful pollutants existing in soil that cannot be biodegraded or thermally degraded; thus, heavy metals pose a threat to environmental quality and humans. To solve the environmental pollution of soil heavy metals, we utilize kriging interpolation to determine the geological distribution of the environmental pollution of metal elements and analyze the main causes of soil heavy metal pollution. Next, the propagation characteristics and diffusion process of heavy metal pollutants are thoroughly analyzed; in addition, an improved one-dimensional convective dispersion model and an improved air subsidence model are established, and real urban soil data are taken as an example for the fitting test. The results show that the improved models that consider more factors, such as adsorption or decomposition factors during the process of convective dispersion, absorption and expulsion factors of the crop root and topographic factors and height changes during the process of air subsidence, are effective. This paper is helpful for distinguishing the primary pollution sources and migration routes of soil metal element pollution and provides a certain reference value for protecting the environment and reducing heavy metal pollution.