Assessing the error of low-cost particulate matter (PM) sensors has been difficult as each empirical study presents unique limitations. Here, we present a new, open-sourced, physics-based model (opcsim) and use it to understand how the properties of different particle sensors alter their accuracy. We offer a summary of likely sources of error for different sensor types, environmental conditions, and particle classes and offer recommendations for the choice of optimal calibrant.

During the Asian summer monsoon period, air pollutants are transported from layers near the ground to high altitudes of 13 to 18 km in the atmosphere. Infrared measurements have shown that particles composed of solid ammonium nitrate are a major part of these pollutants. To enable the quantitative analysis of the infrared spectra, we have determined for the first time accurate optical constants of ammonium nitrate for the low temperature conditions of the upper atmosphere.

Online mass spectrometry produces large amounts of data. These data can be interpreted with statistical methods, enabling scientists to more easily understand the underlying processes. We compared these techniques on car exhaust measurements. We show differences and similarities between the methods and give recommendations on applicability of the methods on certain types of data. We show that applying multiple methods leads to more robust results, thus increasing reliability of the findings.

We present a new correction algorithm that addresses biases in measurements of aerosol light absorption by filter-based photometers, incorporating the transmission of light through the filter and some aerosol optical properties. It was developed using biomass burning aerosols and tested using rural ambient aerosols. This new algorithm is applicable to any filter-based photometer, resulting in good agreement between different colocated instruments in both the laboratory and the field.

The partitioning of noxious chemical compounds between the particulate and gas phases in combustion emissions is key to delineate their exact impacts on atmospheric chemistry and human health. We developed a two-filter sampling system, a multi-technique analytical approach, and advanced statistical methods to fully characterize the composition of both phases in combustion emissions. We could successfully discriminate samples from a standard soot generator by their volatile–non-volatile species.