Air quality and integration of short-term and long-term pollutant data

Modelling PM10 is an important problem in statistical methodology, above all to explain the PM10 behaviour in space and time, since it has been linked to many adverse effects on human and environmental health. But the large spatial variability of the main traffic-related pollutants, and in particular here the PM10, implies the impossibility of obtaining from the data of the fixed stations a complete pictures of the atmospheric pollution in the urban areas. Information from fixed monitoring stations (long-term measurements) are therefore integrated with the ones deriving from mobile station (short-term measurements). Short-term measurements are incomplete and so it is necessary to integrate …

An aggregate air quality index considering interactions among pollutants

Several countries provide an Air Quality Index (AQI) to communicate air pollution, but there is not a unique and nternationally accepted methodology for constructing it. The most of the proposed indices are based on the USA AQI by EPA and are defined by the value of the pollutant with the highest concentration. For each pollutant, a sub-index is computed by linear interpolation according to the grid in a table, but the breakpoints of such a table may differ from one country to another, as well as the descriptors of each category, the air quality standards, the functions chosen as daily synthesis to aggregate hourly values at each site for each pollutant, and so on. Anyway the main drawback …

Urban PM10 air quality indicator sensitivity

Missing value imputation methods for multilevel data

Spatial misaligned data in environmental processes

Short-term and long-term pollutant data: an integration for decision policy makers

Aggregate air pollution indices: a new proposal

A new aggregate Air Quality Index (I2) to represent the global air pollution situation for a given city/region is proposed. Accounting for simultaneous exposure to common pollutants and their effects on human health, this index overcomes existing AQIs. Its goodness and utility is shown by a simulation plan and by an application to a real dataset on main pollutants.

Outliers in hierarchical models: an application to air pollution data

Weather variables and air pollution via hierarchical linear models

Imputation of missing values in air quality data sets

Environmental misaligned data via HLM