A homemade portable no-blind zone laser detection and ranging (lidar) system was designed to map the three-dimensional (3D) distribution of aerosols based on a dual-field-of-view (FOV) receiver system. This innovative lidar prototype has a space resolution of 7.5 m and a time resolution of 30 s. A blind zone of zero meters, and a transition zone of approximately 60 m were realized with careful optical alignments, and were rather meaningful to the lower atmosphere observation. With a scanning platform, the lidar system was used to locate the industrial pollution sources at ground level. The primary parameters of the transmitter, receivers, and detectors are described in this paper. Acquiring a whole return signal of this lidar system represents the key step to the retrieval of aerosol distribution with applying a linear joining method to the two FOV signals. The vertical profiles of aerosols were retrieved by the traditional Fernald method and verified by real-time observations. To effectively and reliably retrieve the horizontal distributions of aerosols, a composition of the Fernald method and the slope method were applied. In this way, a priori assumptions of even atmospheric conditions and the already-known reference point in the lidar equation were avoided. No-blind-zone vertical in-situ observation of aerosol illustrated a detailed evolution from almost 0 m to higher altitudes. No-blind-zone detection provided tiny structures of pollution distribution in lower atmosphere, which is closely related to human health. Horizontal field scanning experiments were also conducted in the Shandong Province. The results showed a high accuracy of aerosol mass movement by this lidar system. An effective quantitative way to locate pollution sources distribution was paved with the portable lidar system after validation by the mass concentration of suspended particulate matter from a ground air quality station.

Ozone pollution adversely affects human health. In recent years, ozone pollution has become an important research topic and a challenge. By combining differential absorption lidar (DIAL), meteorological gradient tower data, the Weather Research and Forecasting (WRF) model, and the backward trajectories model, changes in vertical ozone concentration, the influencing factors on ozone vertical distribution, and ozone sources were analyzed in Shenzhen from September 20 to 30, 2019. Results indicated that from September 25 to 30, there was obvious residual ozone and transport at night. The analysis of ozone concentration and meteorological factors shows that the temperatures of 291.3–301.8 K and the humidity of 19.4%–71.6% were conducive to ozone formation. As height increased from less than 0.1–0.3 km, the average ozone concentration increased, and the average NO₂ concentration decreased. The analysis of potential ozone sources showed that ozone was mainly transported from the northeast of China and that the highest concentration of ozone mainly comes from southeast of Jiangxi province in southeastern China.

Differential absorption lidar is an advanced tool for investigating tropospheric ozone transport and development. High-quality differential absorption lidar data are the basis for studying the temporal and spatial evolution of ozone pollution. We assessed the quality of the ozone data generated via differential absorption lidar. By correcting the ozone lidar profile in real-time with an atmospheric correction term and comparing the lidar data to ozone data collected using an unmanned aerial vehicle (UAV), we quantified the statistical error of the ozone lidar data in the vertical direction and determined that the data from the two instruments were generally in agreement. To verify the reliability of the ozone lidar system and the atmospheric correction algorithm, we conducted a long-term comparison experiment using data from the Canton Tower. Over the two months, the UAV and lidar data were consistent with one another, which confirmed the viability of the ozone lidar optomechanical structure and the atmospheric correction algorithm, both in real-time and over a given time duration. In addition, we also quantified the relationship between statistical error and signal-to-noise ratio. When the SNR is less than 10, the corresponding statistical error is about 40%. The statistical error was less than 15% when the signal-to-noise ratio was greater than 20, and the statistical error was mostly less than 8% when the signal-to-noise ratio was greater than 40. In general, the statistical error of the differential absorption lidar data was inversely proportional to the signal-to-noise ratio of each echo signal.

The range-resolved differential absorption lidar is a high-precision device to measure the concentration of carbon dioxide. This paper provides a system-wide theoretical analysis method for the performance analysis and parameter optimization of the lidar system using the given parameter range. The scattered echo signal, signal-to-noise ratio, and detection sensitivity were simulated by setting assumed parameters with the HITRAN 2020 database and the US 1976 standard atmosphere model to analyze the detection distance and concentration resolution of the lidar system. The effects of the laser energy, repetition frequency, and photodetector noise were also discussed. The wavelength selection near the absorption line is critical because it controls the height region of the highest sensitivity and the demands on frequency stability. Recommendations for the selection of absorption lines are provided in this paper. A quantitative analysis of each error source provided reasonable error ranges.