An investigation of nighttime variability in air quality of Naples (Italy) using wavelet coherence

Abstract

There exists a huge gape in the accurate assessment of nighttime air quality of metropolitan cities. So, in this article, we used Aerosol Robotic Network (AERONET), and Atmospheric Infrared Sounder (AIRS) data from May 16, 2016, to April 30, 2023, to access the nighttime variability of air quality over Naples (Italy). Maximum value (0.26) of aerosol optical depth (AOD) observed during June is because of relative increase in temperature that causes the scattering of pollutants. High value (1.47) of Angstrom exponent (AE) during January indicates the dominance of fine mode particles. Maximum value of precipitable water (3.0) is observed in August. Furthermore, the classification of nighttime aerosols associated with AOD and AE reveals the dominance (73.23%) of urban/industrial aerosols. In addition, nighttime variability of CH 4 , CO, and O 3 are observed high during October, March, and May, respectively. The 72-h air mass backward trajectories plotted on April 23, 2019, at 1500 m, 1000 m, and 500 m indicate the transportation of pollutants from the western northeastern regions of Africa which indicates the transportation of dust particles transported from the Sahara Desert. Naples has the worst air quality in the last couple of years due to rapid urbanization, biomass burning, and fuel combustion. Wavelet coherence (WTC) analysis reveals that AOD has a significant association with temperature, relative humidity, CH 4 , CO, and O 3 in both the long run and short run. A noticeable region of co-movement is observed in the frequency band of almost 8-16 months.

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Vol.:(0123456789)
1 3
Air Quality, Atmosphere & Health
https://doi.org/10.1007/s11869-023-01440-y
An investigation ofnighttime variability inair quality ofNaples (Italy)
using wavelet coherence
SalmanTariq1,2 · MuhammadKhan1,2
Received: 15 July 2023 / Accepted: 20 September 2023
© The Author(s), under exclusive licence to Springer Nature B.V. 2023
Abstract
There exists a huge gape in the accurate assessment of nighttime air quality of metropolitan cities. So, in this article, we
used Aerosol Robotic Network (AERONET), and Atmospheric Infrared Sounder (AIRS) data from May 16, 2016, to
April 30, 2023, to access the nighttime variability of air quality over Naples (Italy). Maximum value (0.26) of aerosol
optical depth (AOD) observed during June is because of relative increase in temperature that causes the scattering of
pollutants. High value (1.47) of Angstrom exponent (AE) during January indicates the dominance of fine mode particles.
Maximum value of precipitable water (3.0) is observed in August. Furthermore, the classification of nighttime aerosols
associated with AOD and AE reveals the dominance (73.23%) of urban/industrial aerosols. In addition, nighttime vari-
ability of CH4, CO, and O3are observed high during October, March, and May, respectively. The 72-h air mass backward
trajectories plotted on April 23, 2019, at 1500 m, 1000 m, and 500 m indicate the transportation of pollutants from the
western northeastern regions of Africa which indicates the transportation of dust particles transported from the Sahara
Desert. Naples has the worst air quality in the last couple of years due to rapid urbanization, biomass burning, and fuel
combustion. Wavelet coherence (WTC) analysis reveals that AOD has a significant association with temperature, relative
humidity, CH4, CO, and O3 in both the long run and short run. A noticeable region of co-movement is observed in the
frequency band of almost 8–16 months.
Keywords Nighttime air quality· Aerosol optical depth· Remote sensing· Italy
Introduction
Air pollution is causing adverse impacts on urban areas and
their economy around the world (Mahowald etal. 2011;
Mehmood etal. 2021). Pollutants in the air include car-
bon monoxide (CO), ozone (O3), sulphur dioxide (SO2),
nitrogen dioxide (NO2), and particulate matter (PM2.5 and
PM10), all of which are harmful to an individual’s health
(Hosseinpoor etal. 2005; Tariq etal. 2022a). Aerosols are
one of the major causes of air pollution and have severe
effects on human health (Tariq etal. 2023). They are gen-
erated through both anthropogenic and natural processes
across the world as industrial emissions, biomass burn-
ing, vehicular exhausts, sea salts, etc. (Ambade etal. 2021;
Tiwari etal. 2023).
Aerosols can cause a cooling or warming effect in the
atmosphere as the result of absorbing or scattering solar
radiation (Khan etal. 2023). Absorbing aerosols alter
environmental stability by absorbing solar radiation (Zeb
etal. 2019; Niveditha etal. 2336). Furthermore, aerosols
can penetrate the human body’s sensitive organs such as
lungs, brain, and bloodstreams (Mahowald etal. 2011;
Khokhar etal. 2016).
Aerosols are always present in the atmosphere as a result
of the enormously diverse nature of their origins (Khan etal.
2023) and their relatively short life duration (Sakka etal.
2020). Although they are carried by winds, marine aerosol
may be observed over land while continental aerosol can
be noticed over waterways (Sorek-Hamer etal. 2016). The
variability of aerosols is effected by transportation systems,
dispersion of sources, and sinks at local, regional, and global
scales (Ginoux etal. 2012).
* Salman Tariq
salmantariq_pu@yahoo.com
1 Remote Sensing, GIS andClimatic Research Lab (National
Center ofGIS andSpace Applications), Centre forRemote
Sensing, University ofthePunjab, Lahore, Pakistan
2 Department ofSpace Science, University ofthePunjab,
Lahore, Pakistan

Air Quality, Atmosphere & Health
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Aerosols have huge impact on human health, solar
radiations, and climate variability (Tariq etal. 2023). As
a result, accurate and extensive investigations of these
atmospheric aerosol features based on long-term datasets
with high spatiotemporal and spectral resolutions are nec-
essary. Several studies discussed the optical properties of
aerosols (such as AOD and AE) to examine temporal and
spatial variability of aerosols (Bilal etal. 2021; Tariq etal.
2022b, 2023). AOD is used to identify aerosols and is
additionally considered to be an effective tool for assess-
ing particulate matter pollution (Ali etal. 2022).
Environmental pollution is monitored and analyzed from
satellites and ground-based instruments (Yu etal. 2022).
For a long time, satellite data have been used to investigate
optical properties of aerosol at different scales (Li etal.
2022). Because of discrepancies in various satellite data-
sets, there exist a chance of error in these satellites observa-
tions (Ali etal. 2014). Therefore, investigation of aerosol
optical characteristics is not limited to satellite data only. So
ground-based networks such AERONET are considered as
more important way of aerosol observation because of their
high accuracy and easy access to datasets (Lin etal. 2021).
Rapid urbanization, frequent traffic, and overcrowd-
ing in the main metropolitan regions continue to destroy
Italy’s environment and health, with smog becoming an
increasingly peculiar occurrence and Sulphur dioxide
levels declining. In recent years, due to rapid urbaniza-
tion, aerosol loading over European countries has rapidly
increased. Urbanization, transportation, industrial sources,
and insufficient industrial waste management are the major
sources of air pollution in Italy. The most populated and
urbanized cities are facing environmental issues because
of anthropogenic activities (Tariq etal. 2023). Eck etal.
(2003) observed high AOD biomass burning events and
the comparison of optical properties of aerosols for dif-
ferent regions. Recent studies have examined the spectral
properties of aerosols over Italy using both ground and
satellite observations. Anoruo (2022) examined seasonal
monsoon AOD and its characteristics over different sites in
Italy from 2010 to 2019 by using MODIS-retrieved AOD
data. Pironti etal. (2022) analyzed the concentration and
potential effects of H2S, SO2, and
𝛿13
CO2 over the cultural
heritage of Naples and Salerno from January 2015 to April
2015. Damiano etal. (2022) analyzed the aerosol optical
and micro-physical characteristics by using AERONET
data over the Mediterranean area of Naples from 2017 to
2021. Kotsiou etal. (2021) elaborated the PM2.5 and its
correlation meteorological parameters during COVID-19
in four major cities (Rome, Milan, Naples, and Salerno) of
Italy from January 1, 2020, to April 8, 2020. Boselli etal.
(2009) observed the variation of aerosols in Naples during
May 2000 to August 2003.
Wavelet coherence (WTC) is a robust technique to study
the co-movements of two time series over frequency bands.
A WTC approach is a powerful tool for unraveling the
physical principles that generate periodic signals (Song and
Chen 2022). Torrence and Compo (1998) initially proposed
this method to analyze the time series of EI Nino-Southern
Oscillation. Later on, Grinsted etal. (2004) used this tech-
nique to discuss geophysical time series that further dem-
onstrate its ability to discuss different physical phenomena
that underly periodic variability. Nourani etal. (2019) also
used WTC technique to investigate the water level fluctua-
tions in Urmia Lake in north-west Iran. Mehmood etal.
(2023) illustrated the WTC between carbon dioxide emis-
sions and technological innovations for different countries
like France, Italy, UK, Canada, and USA during 1995 to
2018. Albulescu and Mutascu (2021) investigated the co-
movement of fuel prices between France, Germany, and
Italy by using WTC analysis during January 3, 2005, to
November 9, 2020. Chekouri etal. (2021) used the WTC
approach to discuss the relation between CO2 emissions,
energy consumption, and economic growth in Algeria from
1971 to 2018.
Daylight sun photometry is used in the last couple of years
to illustrate the aerosols optical properties. These results were
used to investigate the distribution of aerosols, trend analysis,
and their influences on the climate system from the local to
the global scale (Bilal etal. 2021). Furthermore, these sun
photometer aerosol data and the characteristics of aerosols
are limited to daylight. This situation constricts our knowl-
edge of atmospheric dynamics and might bringdistortions
in a significant number of climatological studies (Tariq etal.
2023). Therefore, the observation of atmospheric pollutants
using passive means like moonlight is also very important.
In recent years, a number of previous studies have analyzed
atmospheric pollutants only during day time due to the una-
vailability of data during nighttime (Nichol etal. 2020; Lin
etal. 2021; ul Haq etal. 2022).
Hence, so far, no thorough investigation has been car-
ried out to explore the nighttime air quality in Naples,
Italy. As the temperature decreases at night causes the
trapping of atmospheric pollutants near the Earth’s sur-
face; therefore, it is equally important to analyze the
nighttime air quality. Therefore, this study assesses the
nighttime air quality in Naples by observing variabil-
ity of aerosol generated from newly developed Lunar
AERONET measurements, trace gases observations,
CALIPSO profiles, and meteorological data from May
16, 2016, to April 30, 2023. Datasets for AOD, AE
(440–870), and the precipitable water (PW) is taken from
AERONET. Atmospheric Infrared Sounder (AIRS) pro-
vides the data for nighttime variability of Ozone (O3),
and carbon monoxide (CO). Data for different types of

Air Quality, Atmosphere & Health
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aerosols are taken from Cloud-Aerosol Lidar and Infrared
Pathfinder Satellite Observation (CALIPSO) to investi-
gate air quality. In this investigation, only the nighttime
datasets were used, and the Lunar AERONET observa-
tions were taken at level 1.5. The WTC of AOD with
temperature, RH, O3, CH4, and CO is also discussed
herein.
Material andmethods
Study area
Italy is a country in southern and western Europe located
between longitude 6° and 19° E and latitude 35° and 47° N
(Perrone etal. 2005). It lies in the center of the Mediterra-
nean Sea (Tiwari etal. 2023). France, Switzerland, Austria,
and Slovenia lie to the north of Italy and are bounded by
the Alpine watershed (Albulescu and Mutascu 2021) con-
sisting of the Italian Peninsula and the two Mediterranean
islands of Sicily and Sardinia to the South (Anoruo 2022).
Italy is the 6th most populous country in Europe having
a population of about 60 million. It covers a landmark of
about 301,230 km2.
Naples is the regional capital of Campania and the third
most populous city in Italy having a population of about
950,000 inhabitants. Naples has a population density of
about 8170 inhabitants per square kilometer (Tiwari etal.
2023) and was the first Italian city to be connected to the
national rail network and Metro line. Since twentieth cen-
tury, Naples is the Italy’s fourth largest economy after Milan,
Rome, and Turin because of rapid industrialization and
urbanization due to the construction of Centro Direzionale
(a business district) and the advancement in the transpor-
tation networks that link Naples with Rome and Salerno.
According to the Koppen climate classification, Naples has
a dry summer climate. Winters are made pleasant by mari-
time elements however,significant rainfall occurs on certain
occasions, especially in the autumn and winter. Summers are
characterized by high temperatures and humidity. Figure1
represents the aqua MODIS image taken on May 19, 2022,
of the AERONET site in Naples (Italy). Transportation, con-
struction sites, industrial haze, influence from factories, and
power plants are the major source of air pollution within and
around Naples.
Datasets andmethodology
Wavelet coherence
WTC is a robust technique to study the co-movements of
two time series over frequency bands. A WTC approach
is a powerful tool for unraveling the physical principles
that generate periodic signals (Song and Chen 2022). It
combines both wavelet and coherence analysis to provide a
detailed and comprehensive understanding between under-
lying processes that generate the two time series. Different
methods like Fourier transforms and WTC can be used to
analyze the time series and to study the frequency of differ-
ent parameters and their effects on water level fluctuations
and air pollution (Nourani etal. 2019). WTC technique is
widely used in analyzing the interaction between various
physical and biological phenomena like analyzing finan-
cial market data and studying the variability of climate
and environmental data. In short WTC is a technique that
provides a detailed analysis of time-varying relationships
and variability of two time series parameters having dif-
ferent frequencies.
Datasets
AERONET retrieves long-term and continuous data on
atmospheric aerosols. This network monitors direct solar,
lunar, and sky radiations at different wavelengths, i.e., 340,
380, 440, 500, 675, 870, 940, and 1020 nm (Eck etal. 2003).
This study uses the newly generated LUNAR datasets (AOD,
AE, and PW) from May 16, 2016, to April 30, 2023, at level
1.5 (https:// aeron et. gsfc. nasa. gov/ cgi- bin/ webto ol_ aod_ v3_
lunar). Datasets of relative humidity (RH), temperature, CO,
and O3 are collected from AIRS, whereas Famine Early Warn-
ing Systems Network (FEWS-NET) Land Data Assimilation
System (hereafter FLDAS) provides the data of wind speed
(WS). The temperature and RH were measured at a spatial
resolution of
1◦
×1◦
, whereas the WS was measured at a
resolution of
0.1
◦
×0.1
◦
across Naples. Additionally, aerosol
sub-types were examined through https:// www- calip so. larc.
nasa. gov/ produ cts/ lidar/ browse_ images/ std_ v4_ index. php?d=
2021. To understand the source location and movement of
aerosols, HYSPLIT backward trajectory analysis is carried
out from open data source https:// www. ready. noaa. go v/ hypub-
bin/ trajt ype. pl? runty pe= archi ve. The data for nighttime active
fire counts and vertical-sounding profiles are collected from
NASA FIRMS (https:// firms. modaps. eosdis. nasa. gov/ downl
oad/ create. php), and NOAA (https:// www. ready. noaa. gov/
READY amet. php) respectively.
Results anddiscussions
Variations inmeteorological parameters
The nighttime variability of monthly mean temperature
and RH over Naples, Italy, from May 16, 2016, to April 30,
2023, is shown in Fig.2. Monthly RH and temperature were
in the ranges of 51.95–70.52% and 0.27–17.36 °C having an

Air Quality, Atmosphere & Health
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average of 61.18% and 8.28 °C, respectively. 17.36 and 0.27
°C were the maximum and minimum temperatures recorded
during August and January, respectively. The RH value is
observed high (70.52%) in November and low (51.93%) in
July. So, the weather during winter is cold and humid while
hot and dry during summer in Naples.
Monthly variability ofAOD, AE, PW, andtheir trend
analysis
Figure3 depicts the monthly variations of AOD, AE,
and PW in Naples during the study period. The varia-
bility of AOD, AE, and PW by using LUNAR algorithm
Fig. 1 Digital elevation map of Italy showing the study area

Air Quality, Atmosphere & Health
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showed considerable variations in Naples. The mean
AOD value is observed high (0.3) during July due to a
relative increase in temperature causing the scattering of
pollutants. Mean AOD values were observed lower dur-
ing winter (Dec., Jan., Feb.) with relatively high humid-
ity causing hygroscopic growth of aerosols. 0.24 was the
second highest value of AOD observed during August.
As with the increase in temperature, the upward move-
ment of pollutants also increases that becomes one of the
major cause of high AOD values (Kotsiou etal. 2021).
The second highest value of AOD during August is justi-
fied because of more perceptible water during the mon-
soon season. Perrone etal. (2005) also found that AOD
values of urban-industrial aerosols were peaked during
spring to autumn using AERONET sun photometer meas-
urements over south-East Italy. They observed the optical
properties of aerosol short interval of time from March
2003 to March 2004 in just southeastern region of Italy.
The lowest value of AOD is observed during December;
this abrupt decrease in AOD is because of a decrease in
temperature and less rainfall. Damiano etal. (2022) also
noted the highest and lowest AOD value in August and
December, respectively, by using AERONET data over
Mediterranean areas in Naples. Boselli etal. (2009) simi-
larly observed the increase in aerosols concentration in
Naples after sun set from May 2000 to August 2003. High
AE value always indicates the presence of fine mode par-
ticles, while low values represent the dominance of coarse
mode particles. As AE < 0.5 represents the dominance of
coarse mode particles (Mahowald etal. 2011; Lyamani
etal. 2015) So, the AE value (1.4673) observed during
January represents the dominance of fine mode particles
Fig. 2 Monthly variability in
nighttime temperature (°C) and
relative humidity (%)
0
10
20
30
40
50
60
70
80
JANFEB MARAPR MAY JUN JULAUG SEPOCT NOVDEC
RH and Temp
RH (%)TEMP (°C)
Fig. 3 Variation in mean
monthly AOD, AE, and PW
0
0.5
1
1.5
2
2.5
3
3.5
JANFEB MARAPR MAY JUN JUL AUGSEP OCTNOV DEC
AOD, AE, and PW
AODPW AE

Air Quality, Atmosphere & Health
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during the winter season. During the winter, the main
causes of air pollution in Naples are vehicular emissions
and biomass burning (Damiano etal. 2022). The PW was
found high (3.0 cm) during August and the lowest (1.0 cm)
during January. Figure4 illustrates the variation in night-
time AOD, AE, and PW during the study period in Naples
having an annual decreasing trend of − 0.001%, − 0.002%,
and − 0.005%, respectively.
Variation ofdifferent types ofaerosols
The percentage distribution of nighttime aerosols in Naples
is shown in Fig.5. As Naples is the 4th largest city in Italy in
terms of economic growth. The economy of Naples and its
surrounding areas is dependent upon industry, agriculture,
tourism, and commerce. So because of rapid urbanization,
the highest nighttime contributors of aerosols over Naples
are urban pollutants (
∼
73.23%) followed by dust, mixed, and
black carbon. Perrone etal. (2005) analysis is in line with
our results that the dominance of absorbing urban-industrial
pollutants over south-east Italy from spring to summer from
March 2003 to March 2004 using AERONET sun photome-
ter measurements. Our results are not in line with Bellantone
etal. (2008), who acquired the dominance of a well-mixed
dust layer over the southeastern region of Italy on June 30
by using AERONET data. Their results are different from
our findings because they used data for short interval (June
29 to July 1, 2005) while we analyzed data from May 16,
2016, to April 30, 2023.
Vertical distribution ofaerosols
Convective dispersion and air direction play an impor-
tant role in transferring pollutant concentrations. Back-
trajectories have been frequently used in various aerosol
investigations (Damiano etal. 2022; Khan etal. 2023),
especially for locating aerosol sources (Toledano etal.
2009). The 72-h air mass backward trajectories at heights
of 500 m (Red line), 1000 m (Blue line), and 1500 m
(Green line) AGL were plotted on the day (April 23, 2019)
when the AOD value peaked (1.0) during the study period
by using HYSPLIT Model (see Fig.6). Air masses at a
Fig. 4 Variations in nighttime
AOD, AE, and PW y = -1E-05x + 0.6636 y = 5E-05x -0.8273
y = -0.0002x + 11.192
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
0
0.5
1
1.5
2
2.5
pw
AOD, AE
Date
AODAE PW Linear (AOD) Linear (AE) Linear (PW)
Fig. 5 Percentage distribution
of different aerosol types during
nighttime
0.00%10.00%20.00%30.00% 40.00%50.00%60.00% 70.00% 80.00%
Urban
Dust
Mixed
Black
Percentage distribuon
Aerosol types

Air Quality, Atmosphere & Health
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height of 1500 m are transported from the western region
of Africa and at a height of 1000 m and 500 m are being
transported from northeastern regions which indicates the
dominance of dust particles transported from the Sahara
desert. Damiano etal. (2022) also analyze the dominance
of Sahara dust particles over Naples on July 22, 2017, by
using HYSPLIT model at an altitude of 2000 m, 4000 m,
6000 m, and 8000 m. Prati etal. (2015) similarly accessed
the air quality over the seaport in Naples and analyze that
the majority of the concentration of SO2 and NO2 is trans-
ported from southwest to northeastern regions which is
evidence of the dominance of emissions from ships on
the air quality in Port area during 2012. Gualtieri etal.
(2020) analyzed that during 2020, wind roses over urban
areas of Italy shows no change in wind patterns as that of
previous years.
Vertical soundings
Vertical soundings, also known as atmospheric soundings
or radiosondes, are measurements conducted to determine
the vertical profile of the temperature, humidity, and pres-
sure along with wind speed in the atmosphere at various
altitudes. These observations are critical for weather fore-
casting, climatic research, and for gaining a precise and
accurate understanding of atmospheric processes. The dry
Fig. 6 HYSPLIT model 72 h
backward trajectory at heights
of 500 m, 1000 m, and 1500 m
AGL on high AOD day

Air Quality, Atmosphere & Health
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adiabatic lapse rate and temperature represented by the
grey and red line along with WS (Tariq etal. 2023) are
shown in Fig.7. The temperature increased with height on
April 23, 2019, indicating an identification of an inversion
layer as shown in Fig.7. Furthermore, the WS also goes
on increasing with height.
Variation innighttime CH4, CO, and O3
The nighttime variability of CH4, CO, and O3 over Naples
are shown in Fig.8. The highest concentration of CH4
(1910.20 ppbv) occurs during October, while the lowest
(1887.13 ppbv) concentration was observed during May
as shown in Fig.8a. Favorable meteorological conditions
like high RH and wetland in Naples, Italy, is one of the
major causes of emission of CH4 by natural processes. In
contrast, the highest concentration of CO (Fig.8b) was
observed 138.083 ppbv during March, while 92.3 ppbv
was the lowest CO concentration observed in July. The
concentration of ozone was observed high in April, May,
June, July, and August, whereas it was low in December
and January. The major reasons for the high concentra-
tion of ozone are high temperature and low humidity.
Moreover, the low concentration of ozone (Fig.8c) dur-
ing December and January is linked with low temperature
and high humidity.
Trend analysis ofnighttime CH4, CO, and O3
The nighttime variability and trend analysis of CH4, CO,
and O3 over Naples is shown in Fig.9. CO shows the
highest (143.31 ppbv) value in March 2018 and the low-
est (90.06 ppbv) value in July 2020 with a decreasing
trend of − 0.00004% /year, while CH4 has an increasing
trend of 0.001% per year having high (1929 ppbv) and
low value (1865.67 ppbv) during February 2023 and May
2016, respectively. O3 shows a high (61.82 ppbv) value in
May 2019 and a low (32.48 ppbv) in December 2022 with
a decreasing trend of − 0.001% year−1. Gualtieri etal.
Fig. 7 Vertical sounding on
high AOD day

Air Quality, Atmosphere & Health
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(2020) similarly analyzed that O3 concentration slightly
decreases in Naples from January 2019 to April 2020.
Wavelet coherence analysis
Figure10 illustrates the WTC of AOD with temperature,
relative humidity, ozone, methane, and carbon monoxide.
WTC is a robust technique to study the co-movements
of two time series over frequency bands. The intercon-
nection between AOD and temperature can be seen in
Fig.10a. The figure shows a significant positive asso-
ciation between the variables in the frequency band of
8–16 from 15 to 50th month of the study period. It can
further be observed that bandwidth increases (5–16) after
50th month (November 2020) of the study period. On the
other hand, a significant negative association is observed
Fig. 8 Variation in nighttime
monthly mean concentrations of
a methane, b carbon monoxide,
and c ozone
1875
1880
1885
1890
1895
1900
1905
1910
1915
JANFEB MARAPR MAYJUN JULAUG SEPOCT NOVDEC
CH4 (ppbv)
Month
a
0
20
40
60
80
100
120
140
160
JANFEB MARAPR MAYJUN JULAUG SEPOCT NOVDEC
CO (ppbv)
Month
b
0
10
20
30
40
50
60
JANFEB MARAPR MAYJUN JULAUG SEPOCT NOVDEC
O3(ppbv)
Month
c

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1 3
between AOD and RH. The bandwidth increases with
time. Small islands are also observed in the frequency
bands of 0–2, 0–4, and 2–3. Figure10c exhibits promi-
nent co-movement of AOD and methane in the frequency
band of 8–15 from 15 to 60th month of the study period.
A small island can also be seen in the frequency band of
1–4 from 20 to 25th month of the study period. Down-
ward arrows indicate that AOD is leading methane. Fig-
ure10d also shows a similar behavior but with carbon
monoxide leading the AOD in the long run. Significant
co-movement of AOD and carbon monoxide can also
be observed in small islands in the frequency bands of
0–2 and 4–6. In these bands, AOD is leading the carbon
monoxide. AOD and ozone show in-phase association as
illustrated in Fig.10e in the wider frequency band of 8–16
from 15 to 60th month of the study period. A small island
can also be observed in the figure in the frequency band
of 0–3 with AOD leading the ozone.
Conclusion
This study examined the nighttime variation of aerosol
optical properties (AOD, AE), CH4, CO, O3, and PW over
Naples from May 16, 2016, to April 30, 2013. Datasets
of meteorological parameters (temperature, RH, and WS)
and trace gases (CH4, CO, and O3) were taken from AIRS
and FLDAS, respectively, to analyze the variations in air
quality over Naples, Italy. Variations of AOD, AE, and PW
were observed maximum during June, January, and August,
respectively. Moreover, the classification of aerosols indi-
cates the dominance of urban pollutants followed by dust,
mixed, and black aerosols. CALIPSO subtype profiles reveal
that the majority of aerosol concentration over Naples dur-
ing the study period was because of anthropogenic emis-
sions like industrial and vehicular exhausts. WTC analysis
reveals that AOD has a significant association both in the
long-run and short-sun with temperature, RH, CH4, CO, and
O3. A prominent region of co-movement was observed in
the frequency band of almost 8–16 months. Strict limita-
tions should be implemented on the usage of low-quality fuel
throughout the city. It is also suggested to relocate industrial
estates out of Naples. So, it is concluded that Naples has
been exposed to a high concentration of aerosols and has
poor quality of air because of rapid urbanization and anthro-
pogenic activities within the city. Moreover, this study will
help policymakers to mitigate issues related to air quality in
Naples, Italy.
In this study due to unavailability of long-term AER-
ONET nighttime data of aerosols for Naples, we used only
a few years data to investigate nighttime variability of air
pollution. Future research is recommended for better under-
standing the bad influences of climate on nighttime air pol-
lution in Naples when long-term data will be available.
Fig. 9 Variations and trends
in nighttime concentrations of
methane, carbon monoxide, and
ozone
y = -5E-05x + 117.62 y = -0.0017x + 118.8
y = 0.0158x + 1206.8
1830
1840
1850
1860
1870
1880
1890
1900
1910
1920
1930
1940
0
20
40
60
80
100
120
140
160
May-16
Sep-16
Jan-17
May-17
Sep-17
Jan-18
May-18
Sep-18
Jan-19
May-19
Sep-19
Jan-20
May-20
Sep-20
Jan-21
May-21
Sep-21
Jan-22
May-22
Sep-22
Jan-23
CH4 (ppbv)
CO, O3 (ppbv)
Month
CO O3 CH4

Air Quality, Atmosphere & Health
1 3
ab
cd
e
Fig. 10 Wavelet coherence of AOD with a temperature, b relative humidity, c methane, d carbon monoxide, and e ozone. The continuous black
lines illustrate the regions of 95% significance level

Air Quality, Atmosphere & Health
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Acknowledgements We acknowledged the NASA for providing us
with the datasets used in this study.
Author contribution Salman Tariq conceptualized and wrote the work.
Muhammad Khan wrote the analysis.
Data availability Data will be available on reasonable request.
Declarations
Ethics approval Not required.
Consent to participate Not applicable.
Consent for publication Not applicable.
Competing interests Not required.
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